Using R in microbial analysis-2
Tao Wen(文涛)
2023-05-09
make phyloseq object (构建phyloseq对象)
# Reading the raw file（原始文件读取）
metadata = read.delim("./data/data.ps/metadata.tsv")
row.names(metadata) = metadata$SampleID
otutab = read.table("./data/data.ps/otutab.txt", header=T, row.names=1, sep="\t", comment.char="", stringsAsFactors = F)
taxonomy = read.table("./data/data.ps/taxonomy.txt", header=T, row.names=1, sep="\t", comment.char="", stringsAsFactors = F)
library(ggtree)
tree = read.tree("./data/data.ps/otus.tree")
library(Biostrings)
rep = readDNAStringSet("./data/data.ps/otus.fa")
# Import phyloseq package（导入phyloseq(ps)R包）
library(phyloseq)
ps = phyloseq(sample_data(metadata),
otu_table(as.matrix(otutab), taxa_are_rows=TRUE),
tax_table(as.matrix(taxonomy)),
phy_tree(tree),
refseq(rep)
)
ps
#> phyloseq-class experiment-level object
#> otu_table() OTU Table: [ 2432 taxa and 18 samples ]
#> sample_data() Sample Data: [ 18 samples by 11 sample variables ]
#> tax_table() Taxonomy Table: [ 2432 taxa by 7 taxonomic ranks ]
#> phy_tree() Phylogenetic Tree: [ 2432 tips and 2431 internal nodes ]
#> refseq() DNAStringSet: [ 2432 reference sequences ]
saveRDS(ps,"./data/data.ps/make.ps.rds")Preparing data analysis (数据分析准备）
Import data (ps object) and R packages（导入数据(ps对象)，R包）
# BiocManager::install("ggthemes")
library(tidyverse)
library(phyloseq)
#--输入和设定构建好的ps文件#-----
ps0 = base::readRDS("./data/dataNEW/ps_16s.rds")
#-扩增子分析需要导入的R包#-----------
library(ggClusterNet)
library(EasyStat)
library(fs)
library(ggthemes)
library(RColorBrewer)#调色板调用包
library(magrittr)
library(MicrobiotaProcess)
library(ggsignif)
library(ggtree)
library(ggtreeExtra)
library(ggstar)
library(MicrobiotaProcess)
library(ggnewscale)
library(grid)
# 建立结构保存一级目录#--------
result_path <- paste("./","/result_and_plot/",sep = "")
fs::dir_create(result_path)
res1path <- paste(result_path,"/Base_diversity_16s",sep = "")
fs::dir_create(res1path)
#-设定两个所用主题
mytheme1 = ggplot2::theme_bw() + ggplot2::theme(
panel.background= ggplot2::element_blank(),
panel.grid= ggplot2::element_blank(),
legend.position="right",
legend.title = ggplot2::element_blank(),
legend.background= ggplot2::element_blank(),
legend.key= ggplot2::element_blank(),
plot.title = ggplot2::element_text(vjust = -8.5,hjust = 0.1),
axis.title.y = ggplot2::element_text(colour = "black"),
axis.title.x = ggplot2::element_text(),
axis.text = ggplot2::element_text(),
axis.text.x = ggplot2::element_text(),
axis.text.y = ggplot2::element_text(),
legend.text = ggplot2::element_text()
)
mytheme2 = ggplot2::theme_bw() + ggplot2::theme(
panel.background= ggplot2::element_blank(),
panel.grid= ggplot2::element_blank(),
legend.position="right",
legend.title = ggplot2::element_blank(),
legend.background= ggplot2::element_blank(),
legend.key= ggplot2::element_blank(),
plot.title = ggplot2::element_text(vjust = -8.5,hjust = 0.1),
axis.title.y = ggplot2::element_text(),
axis.title.x = ggplot2::element_text(),
axis.text = ggplot2::element_text(),
axis.text.x = ggplot2::element_text(angle = 90),
axis.text.y = ggplot2::element_text(),
legend.text = ggplot2::element_text()
)
#---扩增子环境布置
colset1 <- RColorBrewer::brewer.pal(9,"Set1")
colset2 <- RColorBrewer::brewer.pal(12,"Paired")
colset3 <- c(RColorBrewer::brewer.pal(11,"Set1"),RColorBrewer::brewer.pal(9,"Pastel1"))
colset4 = colset3
ps = ps0
#--提取有多少个分组#-----------
gnum = phyloseq::sample_data(ps)$Group %>% unique() %>% length()
gnum
#> [1] 3
# 设定排序顺序
axis_order = phyloseq::sample_data(ps)$Group %>%unique();axis_order
#> [1] "Group1" "Group2" "Group3"
ps = ps0
ps_biost = ggClusterNet::filter_OTU_ps(ps = ps,Top = 500)
#--R 语言做lefse法分析-过滤#----------
ps_Rlefse = ggClusterNet::filter_OTU_ps(ps = ps,Top = 400)
#-------功能预测#----
if (is.null(ps@refseq)) {
Tax4Fun = FALSE
} else if(!is.null(ps@refseq)){
Tax4Fun = TRUE
}
ps.t = ps %>% ggClusterNet::filter_OTU_ps(500)
if (Tax4Fun) {
dir.create("data")
otu = ps.t %>%
# ggClusterNet::filter_OTU_ps(1000) %>%
ggClusterNet:: vegan_otu() %>%
t() %>%
as.data.frame()
# write.table(otu,"./data/otu.txt",quote = F,row.names = T,col.names = T,sep = "\t")
rep = ps.t %>%
# ggClusterNet::filter_OTU_ps(1000) %>%
phyloseq::refseq()
rep
# library(Biostrings)
Biostrings::writeXStringSet(rep,"./data/otu.fa")
ps.t = ps.t
#开头空一格字符保存
write.table("\t", "./data/otu.txt",append = F, quote = F, eol = "", row.names = F, col.names = F)
# 保存统计结果，有waring正常
write.table(otu, "./data/otu.txt", append = T, quote = F, sep="\t", eol = "\n", na = "NA", dec = ".", row.names = T, col.names = T)
}
#-构建保存路径
otupath = paste(res1path,"/OTU/",sep = "");otupath
#> [1] ".//result_and_plot//Base_diversity_16s/OTU/"
dir.create(otupath)Output basic information table（基础信息表格输出）
#--基本表格保存#----------
tabpath = paste(otupath,"/report_table/",sep = "")
dir.create(tabpath)
#--raw otu tab
otu = as.data.frame(t(ggClusterNet::vegan_otu(ps0)))
head(otu)FileName <- paste(tabpath,"/otutab.csv", sep = "")
write.csv(otu,FileName,sep = "")
# tax table
tax = as.data.frame((ggClusterNet::vegan_tax(ps0)))
head(tax)FileName <- paste(tabpath,"/tax.csv", sep = "")
write.csv(otu,FileName,sep = "")
ps0_rela = phyloseq::transform_sample_counts(ps0, function(x) x / sum(x) );ps0_rela
#> phyloseq-class experiment-level object
#> otu_table() OTU Table: [ 37178 taxa and 18 samples ]
#> sample_data() Sample Data: [ 18 samples by 2 sample variables ]
#> tax_table() Taxonomy Table: [ 37178 taxa by 7 taxonomic ranks ]
#> phy_tree() Phylogenetic Tree: [ 37178 tips and 37176 internal nodes ]
#> refseq() DNAStringSet: [ 37178 reference sequences ]
#--norm otu tab
otu_norm = as.data.frame(t(ggClusterNet::vegan_otu(ps0_rela)))
FileName <- paste(tabpath,"/otutab_norm.csv", sep = "")
write.csv(otu_norm,FileName,sep = "")
otutax <- cbind(as.data.frame(t(ggClusterNet::vegan_otu(ps0_rela))),as.data.frame((ggClusterNet::vegan_tax(ps0_rela))))
FileName <- paste(tabpath,"/otutax_norm.csv", sep = "")
write.csv(otutax,FileName,sep = "")
for (i in 2: length(phyloseq::rank_names(ps0))) {
psi <- ggClusterNet::tax_glom_wt(ps = ps0,ranks = phyloseq::rank_names(ps0)[i])
#--raw otu tab
otu = as.data.frame(t(ggClusterNet::vegan_otu(psi)))
FileName <- paste(tabpath,"/otutab",phyloseq::rank_names(ps0)[i],".csv", sep = "")
write.csv(otu,FileName,sep = "")
# tax table
tax = as.data.frame((ggClusterNet::vegan_tax(ps0)))
FileName <- paste(tabpath,"/tax",phyloseq::rank_names(ps0)[i],".csv", sep = "")
write.csv(otu,FileName,sep = "")
psi_rela = phyloseq::transform_sample_counts(psi, function(x) x / sum(x) );psi_rela
#--norm otu tab
otu_norm = as.data.frame(t(ggClusterNet::vegan_otu(psi_rela)))
FileName <- paste(tabpath,"/otutab_norm",phyloseq::rank_names(psi)[i],".csv", sep = "")
write.csv(otu_norm,FileName,sep = "")
otutax <- cbind(as.data.frame(t(ggClusterNet::vegan_otu(psi_rela))),as.data.frame((ggClusterNet::vegan_tax(psi_rela))))
FileName <- paste(tabpath,"/otutax_norm",phyloseq::rank_names(ps0)[i],".csv", sep = "")
write.csv(otutax,FileName,sep = "")
}1.Community diversity analysis（1.群落多样性分析）
alpha diversity（alpha多样性）
alppath = paste(otupath,"/alpha/",sep = "")
dir.create(alppath)
#---------多种指标alpha多样性分析加出图-标记显著性
group = "Group"
# 设定排序顺序
axis_order = phyloseq::sample_data(ps)$Group %>%unique();axis_order
#> [1] "Group1" "Group2" "Group3"
samplesize = min(phyloseq::sample_sums(ps))
if (samplesize == 0) {
print("0 number sequence of some samples")
print("median number were used")
ps11 = phyloseq::rarefy_even_depth(ps,sample.size = samplesize)
} else{
ps11 = phyloseq::rarefy_even_depth(ps,sample.size = samplesize)
}
mapping = phyloseq::sample_data(ps11)
ps11 = phyloseq::filter_taxa(ps11, function(x) sum(x ) >0 , TRUE); ps11
#> phyloseq-class experiment-level object
#> otu_table() OTU Table: [ 27642 taxa and 18 samples ]
#> sample_data() Sample Data: [ 18 samples by 2 sample variables ]
#> tax_table() Taxonomy Table: [ 27642 taxa by 7 taxonomic ranks ]
#> phy_tree() Phylogenetic Tree: [ 27642 tips and 27640 internal nodes ]
#> refseq() DNAStringSet: [ 27642 reference sequences ]
# head(mapping)
colnames(mapping) = gsub(group,"AA", colnames(mapping))
mapping$Group = mapping$AA
mapping$Group = as.factor(mapping$Group)
# mapping$Group
count = as.data.frame(t(ggClusterNet::vegan_otu(ps11)))
alpha=vegan::diversity(count, "shannon")
x = t(count) ##转置，行为样本，列为OTU
# head(x)
Shannon = vegan::diversity(x) ##默认为shannon
Shannon
#> sample1 sample10 sample11 sample12 sample13 sample14 sample15 sample16
#> 7.684689 7.538881 7.471720 7.694849 7.935791 7.936605 7.906994 7.878534
#> sample17 sample18 sample2 sample3 sample4 sample5 sample6 sample7
#> 7.948470 7.930505 7.591619 7.678010 7.900705 7.950561 7.597626 7.251199
#> sample8 sample9
#> 7.412761 6.860036
Inv_Simpson <- vegan::diversity(x, index = "invsimpson")
Inv_Simpson
#> sample1 sample10 sample11 sample12 sample13 sample14 sample15 sample16
#> 649.55743 437.49267 487.99787 456.44928 905.54367 829.72158 878.68700 819.88664
#> sample17 sample18 sample2 sample3 sample4 sample5 sample6 sample7
#> 883.67429 869.93594 732.05775 697.66131 913.51580 889.00396 591.14928 384.69615
#> sample8 sample9
#> 184.66933 79.95058
#计算OTU数量
S <- vegan::specnumber(x);S ##每个样本物种数。等价于S2 = rowSums(x>0)
#> sample1 sample10 sample11 sample12 sample13 sample14 sample15 sample16
#> 7864 7037 6837 8825 10023 10357 9872 10024
#> sample17 sample18 sample2 sample3 sample4 sample5 sample6 sample7
#> 10535 10381 5524 6024 8814 10071 6021 4596
#> sample8 sample9
#> 7899 5259
S2 = rowSums(x>0)
#多样性指标：均匀度Pielou_evenness，Simpson_evenness
Pielou_evenness <- Shannon/log(S)
Simpson_evenness <- Inv_Simpson/S
est <- vegan::estimateR(x)
est <- vegan::estimateR(x)
Richness <- est[1, ]
Chao1 <- est[2, ]
ACE <- est[4, ]
report = cbind(Shannon, Inv_Simpson, Pielou_evenness, Simpson_evenness,
Richness, Chao1,ACE)
head(report)
#> Shannon Inv_Simpson Pielou_evenness Simpson_evenness Richness
#> sample1 7.684689 649.5574 0.8567051 0.08259886 7864
#> sample10 7.538881 437.4927 0.8509916 0.06217034 7037
#> sample11 7.471720 487.9979 0.8461645 0.07137602 6837
#> sample12 7.694849 456.4493 0.8469518 0.05172230 8825
#> sample13 7.935791 905.5437 0.8614027 0.09034657 10023
#> sample14 7.936605 829.7216 0.8584366 0.08011215 10357
#> Chao1 ACE
#> sample1 8433.575 8384.511
#> sample10 7432.983 7365.796
#> sample11 7361.479 7223.233
#> sample12 9579.026 9484.530
#> sample13 10955.385 10910.244
#> sample14 11340.878 11316.716
alp = merge(mapping,report , by="row.names",all=F)
index = c("Shannon","Inv_Simpson","Pielou_evenness","Simpson_evenness" ,"Richness" ,"Chao1","ACE" )
#--多种组合alpha分析和差异分析出图
index= alp
# head(index)
#--提取三个代表指标作图
sel = c(match("Shannon",colnames(index)),match("Richness",colnames(index)),match("Pielou_evenness",colnames(index)))
data = cbind(data.frame(ID = 1:length(index$Group),group = index$Group),index[sel])
# head(data)
result = EasyStat::MuiaovMcomper2(data = data,num = c(3:5))
FileName <- paste(alppath,"/alpha_diversity_different_label.csv", sep = "")
write.csv(result,FileName,sep = "")
FileName <- paste(alppath,"/alpha_diversity_index.csv", sep = "")
write.csv(index,FileName,sep = "")
result1 = EasyStat::FacetMuiPlotresultBox(data = data,num = c(3:5),
result = result,
sig_show ="abc",ncol = 3 )
p1_1 = result1[[1]] +
ggplot2::scale_x_discrete(limits = axis_order) +
mytheme1 +
ggplot2::guides(fill = guide_legend(title = NULL)) +
ggplot2::scale_fill_manual(values = colset1)
p1_1
#如何升级展示-提取数据用小提琴图展示
p1_1 = result1[[2]] %>% ggplot(aes(x=group , y=dd )) +
geom_violin(alpha=1, aes(fill=group)) +
geom_jitter( aes(color = group),position=position_jitter(0.17), size=3, alpha=0.5)+
labs(x="", y="")+
facet_wrap(.~name,scales="free_y",ncol = 3) +
# theme_classic()+
geom_text(aes(x=group , y=y ,label=stat)) +
ggplot2::scale_x_discrete(limits = axis_order) +
mytheme1 +
guides(color=guide_legend(title = NULL),
shape=guide_legend(title = NULL),
fill = guide_legend(title = NULL)
) +
ggplot2::scale_fill_manual(values = colset1)
p1_1
res = EasyStat::FacetMuiPlotresultBar(data = data,num = c(3:5),result = result,sig_show ="abc",ncol = 3)
p1_2 = res[[1]]+ scale_x_discrete(limits = axis_order) + guides(color = FALSE) +
mytheme1+
guides(fill = guide_legend(title = NULL))+
scale_fill_manual(values = colset1)
p1_2
res = EasyStat::FacetMuiPlotReBoxBar(data = data,num = c(3:5),result = result,sig_show ="abc",ncol = 3)
p1_3 = res[[1]]+ scale_x_discrete(limits = axis_order) +
mytheme1 +
guides(fill = guide_legend(title = NULL))+
scale_fill_manual(values = colset1)
p1_3
FileName <- paste(alppath,"Alpha_Facet_box", ".pdf", sep = "")
ggsave(FileName, p1_1, width = ((1 + gnum) * 3), height =4,limitsize = FALSE)
FileName <- paste(alppath,"Alpha_Facet_bar", ".pdf", sep = "")
ggsave(FileName, p1_2, width = ((1 + gnum) * 3), height = 4,limitsize = FALSE)
FileName <- paste(alppath,"Alpha_Facet_boxbar", ".pdf", sep = "")
ggsave(FileName, p1_3, width = ((1 + gnum) * 3), height = 4,limitsize = FALSE)
FileName <- paste(alppath,"Alpha_Facet_box", ".jpg", sep = "")
ggsave(FileName, p1_1, width = ((1 + gnum) * 3), height =4,limitsize = FALSE)
FileName <- paste(alppath,"Alpha_Facet_bar", ".jpg", sep = "")
ggsave(FileName, p1_2, width = ((1 + gnum) * 3), height = 4,limitsize = FALSE)
FileName <- paste(alppath,"Alpha_Facet_boxbar", ".jpg", sep = "")
ggsave(FileName, p1_3, width = ((1 + gnum) * 3), height = 4,limitsize = FALSE)
#--总体差异检测alpha多样性
krusk1 = ggpubr::compare_means( Shannon ~ group, data=data, method = "kruskal.test")
krusk2 = ggpubr::compare_means( Richness ~ group, data=data, method = "kruskal.test")
krusk3 = ggpubr::compare_means( Pielou_evenness ~ group, data=data, method = "kruskal.test")
dat = rbind(krusk1,krusk2,krusk3) %>% as.data.frame()
FileName <- paste(alppath,"/alpha_diversity_index_all_p_Kruskal-Wallis.csv", sep = "")
write.csv(dat,FileName,sep = "")alpha-rarefaction curve（alpha稀释曲线）
rare <- mean(phyloseq::sample_sums(ps))/10
method = "Richness"
group = "Group"
start = 100
step = 3000
# library(microbiome)
phyRare = function(ps = ps,N = 3000){
otb = as.data.frame(t(ggClusterNet::vegan_otu(ps)))
otb1 = vegan::rrarefy(t(otb), N)
ps = phyloseq::phyloseq(phyloseq::otu_table(as.matrix(otb1),taxa_are_rows = F),
phyloseq::sample_data(ps)
)
return(ps)
}
#---- 全部指标#----
all = c("observed" , "chao1" , "diversity_inverse_simpson" , "diversity_gini_simpson",
"diversity_shannon" , "diversity_fisher" , "diversity_coverage" , "evenness_camargo",
"evenness_pielou" , "evenness_simpson" , "evenness_evar" , "evenness_bulla",
"dominance_dbp" , "dominance_dmn" , "dominance_absolute" , "dominance_relative",
"dominance_simpson" , "dominance_core_abundance" , "dominance_gini" , "rarity_log_modulo_skewness",
"rarity_low_abundance" , "rarity_noncore_abundance", "rarity_rare_abundance")
#--- 运行计算#----
for (i in seq(start,max(phyloseq::sample_sums(ps)), by = step) ) {
psRe = phyRare(ps = ps, N = i)
if (method == "Richness") {
count = as.data.frame(t(ggClusterNet::vegan_otu(psRe)))
# head(count)
x = t(count) ##转置，行为样本，列为OTU
est = vegan::estimateR(x)
index = est[1, ]
}
if (method %in% c("ACE")) {
ap_phy = phyloseq::estimate_richness(psRe, measures =method)
# head(ap_phy)
index = ap_phy$ACE
}
if (method %in% all) {
alp_mic = microbiome::alpha(psRe,index=method)
# head(alp_mic)
index = alp_mic[,1]
}
tab = data.frame(ID = names(phyloseq::sample_sums(psRe)))
#得到多样性的列
tab = cbind(tab,i,index)
# head(tab)
if (i == start) {
result = tab
}
if (i != start) {
result = rbind(result,tab)
}
}
for (ii in 1:length(phyloseq::sample_sums(ps))) {
result$i[result$i > phyloseq::sample_sums(ps)[ii][[1]]]
df_filter= filter(result, ID ==names(phyloseq::sample_sums(ps)[ii]) &i > phyloseq::sample_sums(ps)[ii][[1]])
result$index
result$index[result$i>phyloseq::sample_sums(ps)[ii][[1]]]
a = result$i>phyloseq::sample_sums(ps)[ii][[1]]
a[a == FALSE] = "a"
b = result$ID == names(phyloseq::sample_sums(ps)[ii])
b[b == FALSE] = "b"
result$index[a== b] = NA
}
#----直接给列，添加分组#----
map = as.data.frame(phyloseq::sample_data(ps))
result$Group = map$Group
## 绘制稀释曲线
main_theme =theme(panel.grid.major=element_blank(),
panel.grid.minor=element_blank(),
plot.title = element_text(vjust = -8.5,hjust = 0.1),
axis.title.y =element_text(size = 7,face = "bold",colour = "black"),
axis.title.x =element_text(size = 7,face = "bold",colour = "black"),
axis.text = element_text(size = 7,face = "bold"),
axis.text.x = element_text(colour = "black",size = 7),
axis.text.y = element_text(colour = "black",size = 7),
legend.text = element_text(size = 7,face = "bold")
)
head(result) result = result %>% dplyr::filter(index != "NA")
result$ID = as.factor(result$ID)
p = ggplot(data= result,aes(x = i,y = index,group = ID,color = Group)) +
geom_line() +
labs(x= "",y=method,title="") +theme_bw()+main_theme
p
#---分组求均值和标准误+se#---
data = result
groups= dplyr::group_by(data, Group,i)
data2 = dplyr::summarise(groups , mean(index), sd(index))
# head(data2)
colnames(data2) = c(colnames(data2)[1:2],"mean","sd")
# 按组均值绘图
p2 = ggplot(data= data2,aes(x = i,y = mean,colour = Group)) +
geom_line()+
labs(x= "",y=method,title="") +theme_bw()+main_theme
# p2
# 组均值+标准差
p4 = ggplot(data=data2,aes(x = i,y = mean,colour = Group)) +
geom_errorbar(data = data2,aes(ymin=mean - sd, ymax=mean + sd,colour = Group),alpha = 0.4, width=.1)+labs(x= "",y=method,title="") +theme_bw()+main_theme
p4
# return(list(p,table = result,p2,p4))
#--提供单个样本溪稀释曲线的绘制
p2_1 <- p +
mytheme1 +
guides(fill = guide_legend(title = NULL))+
scale_fill_manual(values = colset1)
## 提供数据表格，方便输出
raretab <- result
head(raretab)#--按照分组展示稀释曲线
p2_2 <- p2 +
mytheme1 +
guides(fill = guide_legend(title = NULL))+
scale_fill_manual(values = colset1)
#--按照分组绘制标准差稀释曲线
p2_3 <- p4 +
mytheme1 +
guides(fill = guide_legend(title = NULL))+
scale_fill_manual(values = colset1)
FileName <- paste(alppath,"Alpha_rare_sample", ".pdf", sep = "")
ggsave(FileName, p2_1, width = 8, height =6)
FileName <- paste(alppath,"Alpha_rare_group", ".pdf", sep = "")
ggsave(FileName, p2_2, width = 8, height =6)
FileName <- paste(alppath,"Alpha_rare_groupwithSD", ".pdf", sep = "")
ggsave(FileName, p2_3, width = 8, height =6)
FileName <- paste(alppath,"Alpha_rare_sample", ".jpg", sep = "")
ggsave(FileName, p2_1, width = 8, height =6)
FileName <- paste(alppath,"Alpha_rare_group", ".jpg", sep = "")
ggsave(FileName, p2_2, width = 8, height =6)
FileName <- paste(alppath,"Alpha_rare_groupwithSD", ".jpg", sep = "")
ggsave(FileName, p2_3, width = 8, height =6)
FileName <- paste(alppath,"/Alpha_rare_data.csv", sep = "")
write.csv(raretab,FileName,sep = "")beta diversity（beta多样性）
betapath = paste(otupath,"/beta/",sep = "")
dir.create(betapath)
group = "Group"
dist = "bray"
method ="PCoA"
Micromet = "adonis"
pvalue.cutoff = 0.05
pair=TRUE
# 求取相对丰度#----
ps1_rela = phyloseq::transform_sample_counts(ps, function(x) x / sum(x) )
# 排序方法选择#----
#---------如果选用DCA排序
if (method == "DCA") {
# method = "DCA"
ordi = phyloseq::ordinate(ps1_rela, method=method, distance=dist)
#提取样本坐标
points = ordi$rproj[,1:2]
colnames(points) = c("x", "y") #命名行名
#提取特征值
eig = ordi$evals^2
}
#---------CCA排序#----
if (method == "CCA") {
# method = "CCA"
ordi = phyloseq::ordinate(ps1_rela, method=method, distance=dist)
#样本坐标,这里可选u或者v矩阵
points = ordi$CA$v[,1:2]
colnames(points) = c("x", "y") #命名行名
#提取特征值
eig = ordi$CA$eig^2
}
#---------RDA排序#----
if (method == "RDA") {
# method ="RDA"
ordi = phyloseq::ordinate(ps1_rela, method=method, distance=dist)
#样本坐标,这里可选u或者v矩阵
points = ordi$CA$v[,1:2]
colnames(points) = c("x", "y") #命名行名
#提取特征值
eig = ordi$CA$eig
}
#---------DPCoA排序#----
# 不用做了，不选择这种方法了，这种方法运行太慢了
#---------MDS排序#----
if (method == "MDS") {
# method = "MDS"
# ordi = ordinate(ps1_rela, method=ord_meths[i], distance=dist)
ordi = phyloseq::ordinate(ps1_rela, method=method, distance=dist)
#样本坐标
points = ordi$vectors[,1:2]
colnames(points) = c("x", "y") #命名行名
#提取解释度
eig = ordi$values[,1]
}
#---------PCoA排序#----
if (method == "PCoA") {
# method = "PCoA"
unif = phyloseq::distance(ps1_rela , method=dist, type="samples")
#这里请记住pcoa函数
pcoa = stats::cmdscale(unif, k=2, eig=T) # k is dimension, 3 is recommended; eig is eigenvalues
points = as.data.frame(pcoa$points) # 获得坐标点get coordinate string, format to dataframme
colnames(points) = c("x", "y") #命名行名
eig = pcoa$eig
}
#----PCA分析#----
otu_table = as.data.frame(t(ggClusterNet::vegan_otu(ps1_rela )))
# head(otu_table)
# method = "PCA"
if (method == "PCA") {
otu.pca = stats::prcomp(t(otu_table), scale.default = TRUE)
#提取坐标
points = otu.pca$x[,1:2]
colnames(points) = c("x", "y") #命名行名
# #提取荷载坐标
# otu.pca$rotation
# 提取解释度,这里提供的并不是特征值而是标准差，需要求其平方才是特征值
eig=otu.pca$sdev
eig=eig*eig
}
# method = "LDA"
#---------------LDA排序#----
if (method == "LDA") {
#拟合模型
# library(MASS)
data = t(otu_table)
# head(data)
data = as.data.frame(data)
# data$ID = row.names(data)
data = scale(data, center = TRUE, scale = TRUE)
dim(data)
data1 = data[,1:10]
map = as.data.frame(sample_data(ps1_rela))
model = MASS::lda(data, map$Group)
# 提取坐标
ord_in = model
axes = c(1:2)
points = data.frame(predict(ord_in)$x[, axes])
colnames(points) = c("x", "y") #命名行名
# 提取解释度
eig= ord_in$svd^2
}
#---------------NMDS排序#----
# method = "NMDS"
if (method == "NMDS") {
#---------如果选用NMDS排序
# i = 5
# dist = "bray"
ordi = phyloseq::ordinate(ps1_rela, method=method, distance=dist)
#样本坐标,
points = ordi$points[,1:2]
colnames(points) = c("x", "y") #命名行名
#提取stress
stress = ordi$stress
stress= paste("stress",":",round(stress,2),sep = "")
}
#---------------t-sne排序#----
# method = "t-sne"
if (method == "t-sne") {
data = t(otu_table)
# head(data)
data = as.data.frame(data)
# data$ID = row.names(data)
#
data = scale(data, center = TRUE, scale = TRUE)
dim(data)
map = as.data.frame(sample_data(ps1_rela))
# row.names(map)
#---------tsne
# install.packages("Rtsne")
# library(Rtsne)
tsne = Rtsne::Rtsne(data,perplexity = 3)
# 提取坐标
points = as.data.frame(tsne$Y)
row.names(points) = row.names(map)
colnames(points) = c("x", "y") #命名行名
stress= NULL
}
#----差异分析#----
#----整体差异分析#----
g = sample_data(ps)$Group %>% unique() %>% length()
n = sample_data(ps)$Group%>% length()
o = n/g
source("./function/MicroTest.R")
source("./function/pairMicroTest.R")
if (o >= 3) {
title1 = MicroTest(ps = ps1_rela, Micromet = Micromet, dist = dist)
title1
} else{
title1 = NULL
}
#> [1] "Adonis:R 0.364 p: 0.001"
if (F) {
#----两两比较#----
pairResult = pairMicroTest(ps = ps1_rela, Micromet = Micromet, dist = dist)
} else {
pairResult = "no result"
}
#----绘图#----
map = as.data.frame(phyloseq::sample_data(ps1_rela))
map$Group = as.factor(map$Group)
colbar = length(levels(map$Group))
points = cbind(points, map[match(rownames(points), rownames(map)), ])
# head(points)
points$ID = row.names(points)
if (method %in% c("DCA", "CCA", "RDA", "MDS", "PCoA","PCA","LDA")) {
p2 =ggplot(points, aes(x=x, y=y, fill = Group)) +
geom_point(alpha=.7, size=5, pch = 21) +
labs(x=paste0(method," 1 (",format(100*eig[1]/sum(eig),digits=4),"%)"),
y=paste0(method," 2 (",format(100*eig[2]/sum(eig),digits=4),"%)"),
title=title1) +
stat_ellipse(linetype=2,level=0.68,aes(group=Group, colour=Group))
p3 = p2+ggrepel::geom_text_repel(aes(label=points$ID),size = 5)
p3
}
if (method %in% c("NMDS")) {
p2 =ggplot(points, aes(x=x, y=y, fill = Group)) +
geom_point(alpha=.7, size=5, pch = 21) +
labs(x=paste(method,"1", sep=""),
y=paste(method,"2",sep=""),
title=stress)+
stat_ellipse( linetype = 2,level = 0.65,aes(group =Group, colour = Group))
p3 = p2+ggrepel::geom_text_repel( aes(label=points$ID),size=4)
p3
if (method %in% c("t-sne")) {
supp_lab = labs(x=paste(method,"1", sep=""),y=paste(method,"2",sep=""),title=title)
p2 = p2 + supp_lab
p3 = p3 + supp_lab
}
eig = NULL
}
if (method %in% c("t-sne")) {
p2 =ggplot(points, aes(x=x, y=y, fill = Group)) +
geom_point(alpha=.7, size=5, pch = 21) +
labs(x=paste(method,"1", sep=""),
y=paste(method,"2",sep=""))+
stat_ellipse( linetype = 2,level = 0.65,aes(group =Group, colour = Group))
p3 = p2+ggrepel::geom_text_repel( aes(label=points$ID),size=4)
p3
if (method %in% c("t-sne")) {
supp_lab = labs(x=paste(method,"1", sep=""),y=paste(method,"2",sep=""),title=title1)
p2 = p2 + supp_lab
p3 = p3 + supp_lab
}
eig = NULL
}
p3_1 = p2 +
scale_fill_manual(values = colset1)+
scale_color_manual(values = colset1,guide = F) +
mytheme1 +
theme(legend.position = c(0.2,0.2))
p3_1
#带标签图形出图
p3_2 = p3 +
scale_fill_manual(values = colset1)+
scale_color_manual(values = colset1,guide = F) +
mytheme1 +
theme(legend.position = c(0.2,0.2))
p3_2
FileName <- paste(betapath,"/a2_",method,"bray.pdf", sep = "")
ggsave(FileName, p3_1, width = 8, height = 8)
FileName1 <- paste(betapath,"/a2_",method,"",method,"bray.jpg", sep = "")
ggsave(FileName1 , p3_1, width = 12, height = 12)
FileName <- paste(betapath,"/a2_",method,"bray_label.pdf", sep = "")
ggsave(FileName, p3_2, width = 12, height = 12)
FileName1 <- paste(betapath,"/a2_",method,"bray_label.jpg", sep = "")
ggsave(FileName1 , p3_2, width = 12, height = 12)
# 提取出图数据
plotdata = points
FileName <- paste(betapath,"/a2_",method,"bray.csv", sep = "")
write.csv(plotdata,FileName)
#---------排序-精修图
plotdata = points
head(plotdata) # 求均值
cent <- aggregate(cbind(x,y) ~Group, data = plotdata, FUN = mean)
cent # 合并到样本坐标数据中
segs <- merge(plotdata, setNames(cent, c('Group','oNMDS1','oNMDS2')),
by = 'Group', sort = FALSE)
library(ggsci)
p3_3 = p3_1 +geom_segment(data = segs,
mapping = aes(xend = oNMDS1, yend = oNMDS2,color = Group),show.legend=F) + # spiders
geom_point(mapping = aes(x = x, y = y),data = cent, size = 5,pch = 24,color = "black",fill = "yellow") +
scale_fill_manual(values = colset1)+
scale_color_manual(values = colset1,guide = F) +
mytheme1 +
theme(legend.position = c(0.2,0.2))
p3_3
FileName <- paste(betapath,"/a2_",method,"bray_star.pdf", sep = "")
ggsave(FileName, p3_3, width = 8, height = 8)
FileName1 <- paste(betapath,"/a2_",method,"bray_star.jpg", sep = "")
ggsave(FileName1 , p3_3, width = 8, height = 8)
# map
#提取总体比较
TResult = title1
head(TResult)
#> [1] "Adonis:R 0.364 p: 0.001"
FileName <- paste(betapath,"Total_anosim.csv", sep = "")
write.csv(TResult,FileName)
#---普氏分析#------
map = phyloseq::sample_data(ps)
samegroup = map$Group %>% table() %>% as.data.frame() %>% .$Freq %>% unique() %>% length() == 1
method = "spearman"
group = "Group"
ncol = 5
nrow = 2
dist <-
ggClusterNet::scale_micro(ps = ps,method = "rela") %>%
ggClusterNet::vegan_otu()%>%
vegan::vegdist(method="bray") %>%
as.matrix()
map = phyloseq::sample_data(ps)
gru = map[,group][,1] %>% unlist() %>% as.vector()
id = combn(unique(gru),2)
R_mantel = c()
p_mantel = c()
name = c()
R_pro <- c()
p_pro <- c()
plots = list()
for (i in 1:dim(id)[2]) {
id_dist <- row.names(map)[gru == id[1,i]]
dist1 = dist[id_dist,id_dist]
id_dist <- row.names(map)[gru == id[2,i]]
id_dist = id_dist[1:nrow(dist1)]
dist2 = dist[id_dist,id_dist]
mt <- vegan::mantel(dist1,dist2,method = method)
R_mantel[i] = mt$statistic
p_mantel[i] = mt$signif
name[i] = paste(id[1,i],"_VS_",id[2,i],sep = "")
#--p
mds.s <- vegan::monoMDS(dist1)
mds.r <- vegan::monoMDS(dist2)
pro.s.r <- vegan::protest(mds.s,mds.r)
R_pro[i] <- pro.s.r$ss
p_pro[i] <- pro.s.r$signif
Y <- cbind(data.frame(pro.s.r$Yrot), data.frame(pro.s.r$X))
X <- data.frame(pro.s.r$rotation)
Y$ID <- rownames(Y)
p1 <- ggplot(Y) +
geom_segment(aes(x = X1, y = X2, xend = (X1 + MDS1)/2, yend = (X2 + MDS2)/2),
arrow = arrow(length = unit(0, 'cm')),
color = "#B2182B", size = 1) +
geom_segment(aes(x = (X1 + MDS1)/2, y = (X2 + MDS2)/2, xend = MDS1, yend = MDS2),
arrow = arrow(length = unit(0, 'cm')),
color = "#56B4E9", size = 1) +
geom_point(aes(X1, X2), fill = "#B2182B", size = 4, shape = 21) +
geom_point(aes(MDS1, MDS2), fill = "#56B4E9", size = 4, shape = 21) +
labs(title = paste(id[1,i],"-",id[2,i]," ","Procrustes analysis:\n M2 = ",
round(pro.s.r$ss,3),
", p-value = ",
round(pro.s.r$signif,3),
"\nMantel test:\n r = ",
round(R_mantel[i],3),
", p-value =, ",
round(p_mantel[i],3),sep = "") ) + mytheme1
p1
plots[[i]] = p1
}
dat = data.frame(name,R_mantel,p_mantel,R_pro,p_pro )
pp = ggpubr::ggarrange(plotlist = plots, common.legend = TRUE, legend="right",ncol = ncol,nrow = nrow)
mytheme1 = theme_classic() + theme(
panel.background=element_blank(),
panel.grid=element_blank(),
legend.position="right",
legend.title = element_blank(),
legend.background=element_blank(),
legend.key=element_blank(),
# legend.text= element_text(size=7),
# text=element_text(),
# axis.text.x=element_text(angle=45,vjust=1, hjust=1)
plot.title = element_text(vjust = -8.5,hjust = 0.1),
axis.title.y =element_text(size = 15,face = "bold",colour = "black"),
axis.title.x =element_text(size = 15,face = "bold",colour = "black"),
axis.text = element_text(size = 10,face = "bold"),
axis.text.x = element_text(colour = "black",size = 10),
axis.text.y = element_text(colour = "black",size = 10),
legend.text = element_text(size = 10,face = "bold")
)
#--门特尔检验-普氏分析
size = combn(unique(phyloseq::sample_data(ps)$Group),2) %>% dim()
size
#> [1] 2 3
data <- dat
p3_7 <- pp + mytheme1
p3_7
FileName <- paste(betapath,"mantel_pro.csv", sep = "")
write.csv(data,FileName)
FileName1 <- paste(betapath,"/a2_","Mantel_Pro.pdf", sep = "")
ggsave(FileName1 , p3_7, width = size[2] *6, height = 6,limitsize = FALSE)
FileName1 <- paste(betapath,"/a2_","Mantel_Pro.jpg", sep = "")
ggsave(FileName1 , p3_7, width = size[2] *6, height =6,limitsize = FALSE)Species Taxonomic Tree（物种分类树）
library(ggstar)
# library(ggtreeExtra)
library(ggtree)
# library(treeio)
# library(ggstar)
library(ggnewscale)
# #-读取进化树
# library(patchwork)
# library(ggClusterNet)
# library(phyloseq)
# library(tidyverse)
#--微生物组进化树功能
Top_micro = 150
remove_rankID = function(taxtab){
taxtab$Kingdom = gsub("d__","",taxtab$Kingdom)
taxtab$Kingdom = gsub("k__","",taxtab$Kingdom)
taxtab$Phylum = gsub("p__","",taxtab$Phylum)
taxtab$Class = gsub("c__","",taxtab$Class)
taxtab$Order = gsub("o__","",taxtab$Order)
taxtab$Family = gsub("f__","",taxtab$Family)
taxtab$Genus = gsub("g__","",taxtab$Genus)
taxtab$Species = gsub("s__","",taxtab$Species)
return(taxtab)
}
Top = 100
tax = ps %>% vegan_tax() %>%
as.data.frame()
head(tax) tax = remove_rankID(tax) %>%as.matrix()
tax[is.na(tax)] = "Unknown"
tax[tax == " "] = "Unknown"
tax_table(ps) = as.matrix(tax)
alltax = ps %>%
ggClusterNet::filter_OTU_ps(Top) %>%
ggClusterNet::vegan_tax() %>%
as.data.frame()
alltax$OTU = row.names(alltax)
head(alltax)
trda <- MicrobiotaProcess::convert_to_treedata(alltax)
p0 <- ggtree::ggtree(trda, layout="inward_circular", size=0.2, xlim=c(30,NA))
# p0$data
tax = ps %>%
ggClusterNet::filter_OTU_ps(Top) %>%
ggClusterNet::vegan_tax() %>%
as.data.frame()
tippoint = data.frame(OTU = row.names(tax),Taxa = tax$Phylum,Level = "Phylum")
tippoint$OTU = paste("",tippoint$OTU,sep = "")
tippoint$names <- gsub("","",tippoint$OTU)
p0_1 <- ggtree::ggtree(trda, layout="circular", size=0.2, xlim=c(30,NA)) %<+% tippoint
p0_1
p0 <- p0 %<+% tippoint
p0
a <- tippoint$Taxa %>% unique() %>% length()
b = rep(18,a)
names(b) = tippoint$Taxa %>% unique()
p1 <- p0 +
geom_tippoint(
mapping=aes(
color=Taxa,
shape=Level
),
size=1,
alpha=0.8
) +
scale_color_manual(values=colorRampPalette(RColorBrewer::brewer.pal(9,"Set1"))(a),
guide=guide_legend(
keywidth=0.5,
keyheight=0.5,
order=2,
override.aes=list(shape= b,
size=2
),
na.translate=TRUE
)
) +
scale_shape_manual(values=c("Phylum"=20, "Class"=18), guide="none" )
p1_1 <- p0_1 +
geom_tippoint(
mapping=aes(
color=Taxa,
shape=Level
),
size=1,
alpha=0.8
) +
scale_color_manual(values=colorRampPalette(RColorBrewer::brewer.pal(9,"Set1"))(a),
guide=guide_legend(
keywidth=0.5,
keyheight=0.5,
order=2,
override.aes=list(shape= b,
size=2
),
na.translate=TRUE
)
) +
scale_shape_manual(values=c("Phylum"=20, "Class"=18), guide="none" )
#------对OTU之间的关系进行连线
otu = ps %>%
ggClusterNet::filter_OTU_ps(Top) %>%
ggClusterNet::vegan_otu() %>%
t() %>%
as.data.frame()
head(otu) pssub = ps %>%
ggClusterNet::filter_OTU_ps(Top)
result = ggClusterNet::corMicro (ps = pssub ,N = 0,r.threshold=0.8,p.threshold=0.05,method = "pearson")
#--提取相关矩阵
cor = result[[1]]
diag(cor) = 0
# library(tidyfst)
linktab = tidyfst::mat_df(cor) %>%
dplyr::filter(value != 0) %>%
dplyr::mutate(direct = ifelse(value> 0, "positive", "nagetive"))
colnames(linktab) = c("Inhibitor","Sensitive","Interaction","direct")
head(linktab) linktab$Inhibitor = paste("st__",linktab$Inhibitor,sep = "")
linktab$Sensitive = paste("st__",linktab$Sensitive,sep = "")
head(linktab)
# p1$data
p2 <- p1 +
ggnewscale::new_scale_color() +
ggtree::geom_taxalink(
data=linktab,
mapping=aes(
taxa1=Inhibitor,
taxa2=Sensitive,
color=direct
),
alpha=0.6,
offset=0.1,
size=0.15,
ncp=10,
hratio=1,
arrow=grid::arrow(length = unit(0.005, "npc"))
) +
scale_colour_manual(values=c("chocolate2", "#3690C0", "#009E73"),
guide=guide_legend(
keywidth=0.8, keyheight=0.5,
order=1, override.aes=list(alpha=1, size=0.5)
)
)
otu$id = row.names(otu)
ringdat = otu %>% tidyfst::longer_dt(id)
ringdat$value = log2(ringdat$value+1)
ringdat$id = paste("st__",ringdat$id,sep = "")
head(ringdat) num <- ringdat$name %>% unique() %>% length()
p3 <- p2 +
geom_fruit(
data=ringdat,
geom=geom_star,
mapping=aes(
y=id,
x=name,
size=value,
fill= name
),
starshape = 13,
starstroke = 0,
offset=-0.9,
pwidth=0.8,
grid.params=list(linetype=3)
) +
scale_size_continuous(range=c(0, 2),
limits=c(sort(ringdat$value)[2], max(ringdat$value)),
breaks=c(1, 2, 3),
name=bquote(paste(Log[2],"(",.("Count+1"), ")")),
guide=guide_legend(keywidth = 0.4, keyheight = 0.4, order=4,
override.aes = list(starstroke=0.3))
) +
scale_fill_manual(
values=colorRampPalette(RColorBrewer::brewer.pal(12,"Spectral"))(num),
guide=guide_legend(keywidth = 0.4, keyheight = 0.4, order=3)
)
p4 <- p3 +
geom_tiplab(
mapping=aes(
label=names
),
align=TRUE,
size=2,
linetype=NA,
offset=16
)
map = phyloseq::sample_data(pssub)
dat <- pssub %>%
ggClusterNet::vegan_otu() %>%
as.data.frame()
bartab = cbind(dat,data.frame(row.names = row.names(map),ID = row.names(map),Group = map$Group )) %>%
dplyr::group_by(Group) %>%
dplyr::summarise_if(is.numeric,mean) %>%
as.data.frame()%>%
tidyfst::longer_dt(Group)
head(bartab) bartab$name = paste("st__",bartab$name,sep = "")
num = bartab$Group %>% unique() %>%
length()
p5 <- p4 +
ggnewscale::new_scale_fill() +
geom_fruit(
data=bartab,
geom=geom_bar,
mapping=aes(
x=value,
y=name,
fill= Group
),
stat="identity",
orientation="y",
offset=0.48,
pwidth=1.5,
axis.params=list(
axis = "x",
text.angle = -45,
hjust = 0,
vjust = 0.5,
nbreak = 4
)
) +
scale_fill_manual(
name = "Number of interactions",
values=colorRampPalette(RColorBrewer::brewer.pal(12,"Set3"))(num),
guide=guide_legend(keywidth=0.5,keyheight=0.5,order=5)
) +
theme(
legend.background=element_rect(fill=NA),
legend.title=element_text(size=6.5),
legend.text=element_text(size=5),
legend.spacing.y = unit(0.02, "cm"),
legend.margin=ggplot2::margin(0.1, 0.9, 0.1,-0.9, unit="cm"),
legend.box.margin=ggplot2::margin(0.1, 0.9, 0.1, -0.9, unit="cm"),
plot.margin = unit(c(-1.2, -1.2, -1.2, 0.1),"cm")
)
p5_1 <- p1_1 +
new_scale_fill() +
geom_fruit(
data=bartab,
geom=geom_bar,
mapping=aes(
x=value,
y=name,
fill= Group
),
stat="identity",
orientation="y",
offset=0.1,
pwidth=1.5,
axis.params=list(
axis = "x",
text.angle = -45,
hjust = 0,
vjust = 0.5,
nbreak = 4
)
) +
scale_fill_manual(
name = "Number of interactions",
values=colorRampPalette(RColorBrewer::brewer.pal(12,"Set3"))(num),
guide=guide_legend(keywidth=0.5,keyheight=0.5,order=5)
) +
theme(
legend.background=element_rect(fill=NA),
legend.title=element_text(size=6.5),
legend.text=element_text(size=5),
legend.spacing.y = unit(0.02, "cm"),
legend.margin=ggplot2::margin(0.1, 0.9, 0.1,-0.9, unit="cm"),
legend.box.margin=ggplot2::margin(0.1, 0.9, 0.1, -0.9, unit="cm"),
plot.margin = unit(c(-1.2, -1.2, -1.2, 0.1),"cm")
)
library(ggClusterNet)
barpath = paste(otupath,"/phy_tree_micro/",sep = "");print(barpath)
#> [1] ".//result_and_plot//Base_diversity_16s/OTU//phy_tree_micro/"
dir.create(barpath)
p6 = p1_1
p7 = p5_1
detach("package:ggstar")
FileName <- paste(barpath,Top_micro,"phy_tree_micro1", ".pdf", sep = "")
ggsave(FileName, p0, width = 6, height = 6)
FileName <- paste(barpath,Top_micro,"phy_tree_micro2", ".pdf", sep = "")
ggsave(FileName, p1, width = 7, height = 7)
FileName <- paste(barpath,Top_micro,"phy_tree_micro3", ".pdf", sep = "")
ggsave(FileName, p2, width = 7, height = 7)
FileName <- paste(barpath,Top_micro,"phy_tree_micro4", ".pdf", sep = "")
ggsave(FileName, p3, width = 12, height = 12)
FileName <- paste(barpath,Top_micro,"phy_tree_micro5", ".pdf", sep = "")
ggsave(FileName, p4, width = 15, height = 15)
FileName <- paste(barpath,Top_micro,"phy_tree_micro5", ".pdf", sep = "")
ggsave(FileName, p5, width = 18, height = 18)
FileName <- paste(barpath,Top_micro,"phy_tree_micro6", ".pdf", sep = "")
ggsave(FileName, p6, width = 7, height = 7)
FileName <- paste(barpath,Top_micro,"phy_tree_micro7", ".pdf", sep = "")
ggsave(FileName, p7, width = 12, height = 12)
# library(cowplot)
# save_plot(FileName, p2, base_height = 7, base_width =7)
FileName <- paste(barpath,Top_micro,"phy_tree_micro1", ".png", sep = "")
ggsave(FileName, p0, width = 6, height = 6)
FileName <- paste(barpath,Top_micro,"phy_tree_micro2", ".png", sep = "")
ggsave(FileName, p1, width = 7, height = 7)
FileName <- paste(barpath,Top_micro,"phy_tree_micro3", ".png", sep = "")
ggsave(FileName, p2, width = 7, height = 7,dpi = 72)
FileName <- paste(barpath,Top_micro,"phy_tree_micro4", ".png", sep = "")
ggsave(FileName, p3, width = 12, height = 12,dpi = 72)
FileName <- paste(barpath,Top_micro,"phy_tree_micro5", ".png", sep = "")
ggsave(FileName, p4, width = 15, height = 15,dpi = 72)
FileName <- paste(barpath,Top_micro,"phy_tree_micro5", ".png", sep = "")
ggsave(FileName, p5, width = 18, height = 18,dpi = 72)
FileName <- paste(barpath,Top_micro,"phy_tree_micro6", ".png", sep = "")
ggsave(FileName, p6, width = 7, height = 7,dpi = 72)
FileName <- paste(barpath,Top_micro,"phy_tree_micro7", ".png", sep = "")
ggsave(FileName, p7, width = 12, height = 12,dpi = 72)Species composition-chord diagram（物种组成-和弦图）
Top = 10
rank = 7
path = barpath
ps_rela = phyloseq::transform_sample_counts(ps, function(x) x / sum(x) );ps_rela
#> phyloseq-class experiment-level object
#> otu_table() OTU Table: [ 37178 taxa and 18 samples ]
#> sample_data() Sample Data: [ 18 samples by 2 sample variables ]
#> tax_table() Taxonomy Table: [ 37178 taxa by 7 taxonomic ranks ]
#> phy_tree() Phylogenetic Tree: [ 37178 tips and 37176 internal nodes ]
#> refseq() DNAStringSet: [ 37178 reference sequences ]
ps_P <- ps_rela %>%
ggClusterNet::tax_glom_wt( rank = rank)
ps_P
#> phyloseq-class experiment-level object
#> otu_table() OTU Table: [ 1074 taxa and 18 samples ]
#> sample_data() Sample Data: [ 18 samples by 2 sample variables ]
#> tax_table() Taxonomy Table: [ 1074 taxa by 7 taxonomic ranks ]
otu_P = as.data.frame((ggClusterNet::vegan_otu(ps_P)))
head(otu_P) tax_P = as.data.frame(ggClusterNet::vegan_tax(ps_P))
sub_design <- as.data.frame(phyloseq::sample_data(ps_P))
count2 = otu_P
#数据分组
iris.split <- split(count2,as.factor(sub_design$Group))
#数据分组计算平均值
iris.apply <- lapply(iris.split,function(x)colSums(x[]))
# 组合结果
iris.combine <- do.call(rbind,iris.apply)
ven2 = t(iris.combine)
# head(ven2)
lev = phyloseq::rank.names(ps)[rank]
Taxonomies <- ps %>%
ggClusterNet::tax_glom_wt(rank = rank) %>%
phyloseq::transform_sample_counts(function(x) {x/sum(x)} )%>%
phyloseq::psmelt() %>%
#filter(Abundance > 0.05) %>%
dplyr::arrange( !!sym(lev))
iris_groups<- dplyr::group_by(Taxonomies, !!sym(lev))
ps0_sum <- dplyr::summarise(iris_groups, mean(Abundance), sd(Abundance))
ps0_sum[is.na(ps0_sum)] <- 0
head(ps0_sum) colnames(ps0_sum) = c("ID","mean","sd")
ps0_sum <- dplyr::arrange(ps0_sum,desc(mean))
ps0_sum$mean <- ps0_sum$mean *100
ps0_sum <- as.data.frame(ps0_sum)
head(ps0_sum) top_P = ps0_sum$ID[1:Top];top_P
#> [1] "Unassigned" "Gemmatimonas_aurantiaca"
#> [3] "Gaiella_occulta" "Terrimonas_lutea"
#> [5] "Aquicella_siphonis" "Ralstonia_pickettii"
#> [7] "Bacillus_funiculus" "Pseudolabrys_taiwanensis"
#> [9] "Chryseolinea_serpens" "Pseudoduganella_violaceinigra"
### 开始进一步合并过滤
otu_P = as.data.frame(t(otu_P))
otu_tax = merge(ven2,tax_P,by = "row.names",all = F)
dim(otu_tax)
#> [1] 1074 11
otu_tax[,lev] = as.character(otu_tax[,lev])
otu_tax[,lev][is.na(otu_tax[,lev])] = "others"
i = 1
for (i in 1:nrow(otu_tax)) {
if(otu_tax[,lev] [i] %in% top_P){otu_tax[,lev] [i] = otu_tax[,lev] [i]}
else if(!otu_tax[,lev] [i] %in% top_P){otu_tax[,lev] [i] = "others"}
}
otu_tax[,lev] = as.factor(otu_tax[,lev])
head(otu_tax)
otu_mean = otu_tax[as.character(unique(sub_design$Group))]
head(otu_mean) row.names(otu_mean) = row.names(otu_tax)
iris.split <- split(otu_mean,as.factor(otu_tax[,lev]))
#数据分组计算平均值
iris.apply <- lapply(iris.split,function(x)colSums(x[]))
# 组合结果
iris.combine <- do.call(rbind,iris.apply)
mer_otu_mean = t(iris.combine)
head(mer_otu_mean )
#> Aquicella_siphonis Bacillus_funiculus Chryseolinea_serpens
#> Group1 0.04289304 0.02092137 0.02726656
#> Group2 0.03112277 0.06143216 0.02757814
#> Group3 0.03513122 0.01170578 0.02365613
#> Gaiella_occulta Gemmatimonas_aurantiaca others
#> Group1 0.07121335 0.1801137 0.6736228
#> Group2 0.11836097 0.1482847 0.9360435
#> Group3 0.06280437 0.1374640 0.7640420
#> Pseudoduganella_violaceinigra Pseudolabrys_taiwanensis
#> Group1 0.01523503 0.03331843
#> Group2 0.02901883 0.02958219
#> Group3 0.01784443 0.02983005
#> Ralstonia_pickettii Terrimonas_lutea Unassigned
#> Group1 0.05437782 0.03120037 4.849838
#> Group2 0.03491258 0.03582652 4.547838
#> Group3 0.01315802 0.05091932 4.853445
mi_sam = RColorBrewer::brewer.pal(9,"Set1")
mi_tax = colorRampPalette(RColorBrewer::brewer.pal(9,"Set3"))(length(row.names(mer_otu_mean)))
grid.col = NULL
#这里设置样品颜色
grid.col[as.character(unique(sub_design$Group))] = mi_sam
#设置群落中物种水平颜色
# grid.col[colnames(mer_otu_mean)] = mi_tax
grid.col[row.names(mer_otu_mean)] = mi_tax
FileName2 <- paste(path,"/",phyloseq::rank.names(ps)[rank],"_cricle",".pdf", sep = "")
pdf(FileName2, width = 12, height = 8)
#gap.degree修改间隔，不同小块之间的间隔
circlize::circos.par(gap.degree = c(rep(2, nrow(mer_otu_mean)-1), 10, rep(2, ncol(mer_otu_mean)-1), 10),
start.degree = 180)
circlize::chordDiagram(mer_otu_mean,
directional = F,
diffHeight = 0.06,
grid.col = grid.col,
reduce = 0,
transparency = 0.5,
annotationTrack =c("grid", "axis"),
preAllocateTracks = 2
)
circlize::circos.track(track.index = 1, panel.fun = function(x, y) {
circlize::circos.text(circlize::CELL_META$xcenter, circlize::CELL_META$ylim[1], circlize::CELL_META$sector.index,
facing = "clockwise", niceFacing = TRUE, adj = c(0, 0.5))}, bg.border = NA)# here set bg.border to NA is important
circlize::circos.clear()
dev.off()
#> png
#> 2
grid.col = NULL
#这里设置样品颜色
grid.col[as.character(unique(sub_design$Group))] = mi_sam
#设置群落中物种水平颜色
# grid.col[colnames(mer_otu_mean)] = mi_tax
grid.col[row.names(mer_otu_mean)] = mi_tax
FileName2 <- paste(path,"/",phyloseq::rank.names(ps)[rank],"_cricle",".png", sep = "")
png(FileName2, res=150, width = 1000, height = 1008)
#gap.degree修改间隔，不同小块之间的间隔
circlize::circos.par(gap.degree = c(rep(2, nrow(mer_otu_mean)-1), 10, rep(2, ncol(mer_otu_mean)-1), 10),
start.degree = 180)
circlize::chordDiagram(mer_otu_mean,directional = F,
reduce = 0,
diffHeight = 0.06,grid.col = grid.col, transparency = 0.5, annotationTrack =c("grid", "axis"),
preAllocateTracks = 2
)
circlize::circos.track(track.index = 1, panel.fun = function(x, y) {
circlize::circos.text(circlize::CELL_META$xcenter, circlize::CELL_META$ylim[1], circlize::CELL_META$sector.index,
facing = "clockwise", niceFacing = TRUE, adj = c(0, 0.5))}, bg.border = NA) # here set bg.border to NA is important
circlize::circos.clear()
dev.off()
#> png
#> 2Species composition-stack bar diagram（物种组成-堆叠柱状图）
library(ggalluvial)
group = "Group"
j = "Phylum"
label = TRUE
sd = FALSE
Top = 10
tran = TRUE
# axis_order = NA
# psdata = ps %>%
# tax_glom(taxrank = j)
psdata <- ggClusterNet::tax_glom_wt(ps = ps,ranks = j)
# transform to relative abundance
if (tran == TRUE) {
psdata = psdata%>%
phyloseq::transform_sample_counts(function(x) {x/sum(x)} )
}
otu = phyloseq::otu_table(psdata)
tax = phyloseq::tax_table(psdata)
for (i in 1:dim(tax)[1]) {
if (row.names(tax)[i] %in% names(sort(rowSums(otu), decreasing = TRUE)[1:Top])) {
tax[i,j] =tax[i,j]
} else {
tax[i,j]= "others"
}
}
phyloseq::tax_table(psdata)= tax
Taxonomies <- psdata %>% # Transform to rel. abundance
phyloseq::psmelt()
Taxonomies$Abundance = Taxonomies$Abundance * 100
# Taxonomies$Abundance = Taxonomies$Abundance /rep
colnames(Taxonomies) <- gsub(j,"aa",colnames(Taxonomies))
data = c()
i = 2
for (i in 1:length(unique(phyloseq::sample_data(ps)$Group))) {
a <- as.data.frame(table(phyloseq::sample_data(ps)$Group))[i,1]
b = as.data.frame(table(phyloseq::sample_data(ps)$Group))[i,2]
c <- Taxonomies %>%
dplyr::filter(Group == a)
c$Abundance <- c$Abundance/b
data = data.frame(Sample =c$Sample,Abundance = c$Abundance,aa =c$aa,Group = c$Group)
if (i == 1) {
table = data
}
if (i != 1) {
table = rbind(table,data)
}
}
Taxonomies = table
#按照分组求均值
by_cyl <- dplyr::group_by(Taxonomies, aa,Group)
zhnagxu2 = dplyr::summarise(by_cyl, sum(Abundance), sd(Abundance))
iris_groups<- dplyr::group_by(Taxonomies, aa)
cc<- dplyr::summarise(iris_groups, sum(Abundance))
head(cc) colnames(cc)= c("aa","allsum")
cc<- dplyr::arrange(cc, desc(allsum))
##使用这个属的因子对下面数据进行排序
head(zhnagxu2) colnames(zhnagxu2) <- c("aa","group","Abundance","sd")
zhnagxu2$aa = factor(zhnagxu2$aa,order = TRUE,levels = cc$aa)
zhnagxu3 = zhnagxu2
##制作标签坐标，标签位于顶端
# Taxonomies_x = ddply(zhnagxu3,"group", summarize, label_y = cumsum(Abundance))
# head(Taxonomies_x )
#标签位于中部
# Taxonomies_x1 = ddply(zhnagxu3,"group", transform, label_y = cumsum(Abundance) - 0.5*Abundance)
Taxonomies_x = plyr::ddply(zhnagxu3,"group", summarize,label_sd = cumsum(Abundance),label_y = cumsum(Abundance) - 0.5*Abundance)
head( Taxonomies_x )
# Taxonomies_x$label_y =
Taxonomies_x = cbind(as.data.frame(zhnagxu3),as.data.frame(Taxonomies_x)[,-1])
Taxonomies_x$label = Taxonomies_x$aa
# #使用循环将堆叠柱状图柱子比较窄的别写标签，仅仅宽柱子写上标签
# for(i in 1:nrow(Taxonomies_x)){
# if(Taxonomies_x[i,3] > 3){
# Taxonomies_x[i,5] = Taxonomies_x[i,5]
# }else{
# Taxonomies_x[i,5] = NA
# }
# }
Taxonomies_x$aa = factor(Taxonomies_x$aa,order = TRUE,levels = c(as.character(cc$aa)))
##普通柱状图
p4 <- ggplot(Taxonomies_x , aes(x = group, y = Abundance, fill = aa, order = aa)) +
geom_bar(stat = "identity",width = 0.5,color = "black") +
# scale_fill_manual(values = colors) +
theme(axis.title.x = element_blank()) +
theme(legend.text=element_text(size=6)) +
scale_y_continuous(name = "Relative abundance (%)") +
guides(fill = guide_legend(title = j)) +
labs(x="",y="Relative abundance (%)",
title= "")
# paste("Top ",Top," ",j,sep = "")
p4
p4 = p4 + scale_x_discrete(limits = axis_order)
if (sd == TRUE) {
p4 = p4 +
geom_errorbar(aes(ymin=label_sd-sd, ymax=label_sd +sd), width=.2)
}
if (label == TRUE) {
p4 = p4 +
geom_text(aes(y = label_y, label = label ))
}
map = as.data.frame(phyloseq::sample_data(ps))
if (length(unique(map$Group))>3){p4 = p4 + theme(axis.text.x=element_text(angle=45,vjust=1, hjust=1))}
#-------------冲击图
cs = Taxonomies_x $aa
lengthfactor <- cs %>%
levels() %>%
length()
cs4 <- cs %>%
as.factor() %>%
summary() %>%
as.data.frame()
cs4$id = row.names(cs4)
#对因子进行排序
df_arrange<- dplyr::arrange(cs4, id)
#对Taxonomies_x 对应的列进行排序
Taxonomies_x1<- dplyr::arrange(Taxonomies_x , aa)
head(Taxonomies_x1) #构建flow的映射列Taxonomies_x
Taxonomies_x1$ID = factor(rep(c(1:lengthfactor), cs4$.))
#colour = "black",size = 2,,aes(color = "black",size = 0.8)
head(Taxonomies_x1) Taxonomies_x1$Abundance
#> [1] 39.237397 40.722278 40.228699 15.599818 12.611327 16.144444 16.726732
#> [8] 13.155390 14.172527 6.017195 9.123618 7.533416 6.089171 6.210184
#> [15] 6.063844 3.576966 4.740252 3.342673 3.661095 3.682421 4.121777
#> [22] 3.012819 3.265553 3.139758 3.001894 2.471411 2.291067 1.632678
#> [29] 2.740635 1.464264 1.444234 1.276930 1.497529
p3 <- ggplot(Taxonomies_x1, aes(x = group, y = Abundance,fill = aa,alluvium = aa,stratum = ID)) +
ggalluvial::geom_flow(aes(fill = aa, colour = aa),
stat = "alluvium", lode.guidance = "rightleft",
color = "black",size = 0.2,width = 0.35,alpha = .2) +
geom_bar(width = 0.45,stat = "identity") +
labs(x="",y="Relative abundance (%)",
title= "") +
guides(fill = guide_legend(title = j),color = FALSE) +
scale_y_continuous(expand = c(0,0))
p3
# flower plot
p1 <- ggplot(Taxonomies_x1,
aes(x = group, alluvium = aa, y = Abundance)) +
ggalluvial::geom_flow(aes(fill = aa, colour = aa), width = 0) +
labs(x="",y="Relative abundance (%)",
title="") +
guides(fill = guide_legend(title = j),color = FALSE) +
scale_y_continuous(expand = c(0,0))
p1 = p1 + scale_x_discrete(limits = axis_order)
p3 = p3 + scale_x_discrete(limits = axis_order)
# p3
if (label == TRUE) {
p1 = p1 +
geom_text(aes(y = label_y, label = label ))
p3 = p3 +
geom_text(aes(y = label_y, label = label ))
}
if (sd == TRUE) {
p1 = p1 +
geom_errorbar(aes(ymin=label_sd-sd, ymax=label_sd +sd), width=.2)
p3 = p3 +
geom_errorbar(aes(ymin=label_sd-sd, ymax=label_sd +sd), width=.2)
}
if (length(unique(map$Group))>3){ p3=p3+theme(axis.text.x=element_text(angle=45,vjust=1, hjust=1))}
# return(list(p4,Taxonomies,p3,p1))
barpath = paste(otupath,"/Microbial_composition/",sep = "")
dir.create(barpath)
phyloseq::rank_names(ps)
#> [1] "Kingdom" "Phylum" "Class" "Order" "Family" "Genus" "Species"
p4_1 <- p4 +
# scale_fill_brewer(palette = "Paired") +
scale_fill_manual(values = colset3) +
scale_x_discrete(limits = axis_order) +
mytheme1
p4_1
p4_2 <- p3 +
# scale_fill_brewer(palette = "Paired") +
scale_fill_manual(values = colset3) +
scale_x_discrete(limits = axis_order) +
mytheme1
p4_2
databar <- Taxonomies
FileName1 <- paste(barpath,"/a2_",j,"_barflow",".pdf", sep = "")
ggsave(FileName1, p4_2, width = (5+ gnum), height =8 )
FileName2 <- paste(barpath,"/a2_",j,"_barflow",".jpg", sep = "")
ggsave(FileName2, p4_2, width = (5+ gnum), height =8 )
FileName1 <- paste(barpath,"/a2_",j,"_bar",".pdf", sep = "")
ggsave(FileName1, p4_1, width = (5+ gnum), height =8 )
FileName2 <- paste(barpath,"/a2_",j,"_bar",".jpg", sep = "")
ggsave(FileName2, p4_1, width = (5+ gnum), height =8 )
FileName <- paste(barpath,"/a2_",j,"_bar_data",".csv", sep = "")
write.csv(databar,FileName)
detach("package:ggalluvial")Species composition-circle strack bar diagram（物种组成-环状堆叠柱状图）
# BiocManager::install("ggstance")
ps = readRDS("./data/dataNEW/ps_16s.rds")
library(ggtree)
barpath = paste(otupath,"/circle_Micro_strack_bar/",sep = "");print(barpath)
#> [1] ".//result_and_plot//Base_diversity_16s/OTU//circle_Micro_strack_bar/"
dir.create(barpath)
Top = 15
dist = "bray"
cuttree = 3
hcluter_method = "complete"
# phyloseq(ps)对象标准化
otu = ps %>%
ggClusterNet::scale_micro() %>%
ggClusterNet::vegan_otu() %>% t() %>%
as.data.frame()
# 导出OTU表
# otu = as.data.frame(t(vegan_otu(ps1_rela)))
#计算距离矩阵
unif = phyloseq::distance(ps %>% ggClusterNet::scale_micro() , method = dist)
# 聚类树，method默认为complete
hc <- stats::hclust(unif, method = hcluter_method )
# take grouping with hcluster tree
clus <- cutree(hc, cuttree )
d = data.frame(label = names(clus),
member = factor(clus))
# eatract mapping file
map = as.data.frame(phyloseq::sample_data(ps))
dd = merge(d,map,by = "row.names",all = F)
row.names(dd) = dd$Row.names
dd$Row.names = NULL
# library(tidyverse)
# ggtree绘图 #----
p = ggtree::ggtree(hc, layout='circular') %<+% dd +
geom_tippoint(size=5, shape=21, aes(fill= Group, x=x)) +
geom_tiplab(aes(color = Group,x=x * 1.2), hjust=1,offset=0.3) + xlim(-0.5,NA)
p
psdata = ggClusterNet::tax_glom_wt(ps = ps %>% ggClusterNet::scale_micro(),ranks = "Phylum" )
# 转化丰度值
psdata = psdata%>% phyloseq::transform_sample_counts(function(x) {x/sum(x)} )
#--提取otu和物种注释表格
otu = phyloseq::otu_table(psdata)
tax = phyloseq::tax_table(psdata)
head(tax)
#> Taxonomy Table: [6 taxa by 2 taxonomic ranks]:
#> Kingdom Phylum
#> Acidobacteria "Bacteria" "Acidobacteria"
#> Actinobacteria "Bacteria" "Actinobacteria"
#> Aminicenantes "Bacteria" "Aminicenantes"
#> Armatimonadetes "Bacteria" "Armatimonadetes"
#> Bacteroidetes "Bacteria" "Bacteroidetes"
#> BRC1 "Bacteria" "BRC1"
#--按照指定的Top数量进行筛选与合并
j = "Phylum"
for (i in 1:dim(tax)[1]) {
if (row.names(tax)[i] %in% names(sort(rowSums(otu), decreasing = TRUE)[1:Top])) {
tax[i,j] =tax[i,j]
} else {
tax[i,j]= "Other"
}
}
phyloseq::tax_table(psdata)= tax
Taxonomies <- psdata %>% phyloseq::psmelt()
Taxonomies$Abundance = Taxonomies$Abundance * 100
Taxonomies$OTU = NULL
colnames(Taxonomies)[1] = "id"
head(Taxonomies)
dat2 = data.frame(id = Taxonomies$id,Abundance = Taxonomies$Abundance,Phylum = Taxonomies$Phylum)
head(dat2)
p2 <- p +
ggnewscale::new_scale_fill() +
ggtreeExtra::geom_fruit(
data=dat2,
geom=geom_bar,
mapping=aes(
x=Abundance,
y=id,
fill= Phylum
),
stat="identity",
width = 0.4,
orientation="y",
offset=0.9,
pwidth=2,
axis.params=list(
axis = "x",
text.angle = -45,
hjust = 0,
vjust = 0.5,
nbreak = 4
)
) +
scale_fill_manual(
# name = "Number of interactions",
values=c(colset3),
guide=guide_legend(keywidth=0.5,keyheight=0.5,order=5)
) +theme_void()
FileName2 <- paste(barpath,"/a2_","_bar",".jpg", sep = "")
ggsave(FileName2, p2, width = 10, height =8 )
FileName2 <- paste(barpath,"/a2_","_bar",".pdf", sep = "")
ggsave(FileName2, p2, width = 10, height =8 )
#--距离和丰度合并#----
dist = "bray"
j = "Phylum"# 使用门水平绘制丰度图表
# rep = 6 # 重复数量是6个
Top = 10 # 提取丰度前十的物种注释
tran = TRUE # 转化为相对丰度值
hcluter_method = "complete"
Group = "Group"
cuttree = 3
# phyloseq(ps)对象标准化
ps1_rela = phyloseq::transform_sample_counts(ps, function(x) x / sum(x) )
# 导出OTU表
otu = as.data.frame(t(ggClusterNet::vegan_otu(ps1_rela)))
#计算距离矩阵
unif = phyloseq::distance(ps1_rela , method = dist)
# 聚类树，method默认为complete
hc <- stats::hclust(unif, method = hcluter_method )
# take grouping with hcluster tree
clus <- stats::cutree(hc, cuttree )
# 提取树中分组的标签和分组编号
d = data.frame(label = names(clus),
member = factor(clus))
# eatract mapping file
map = as.data.frame(phyloseq::sample_data(ps))
# 合并树信息到样本元数据
dd = merge(d,map,by = "row.names",all = F)
row.names(dd) = dd$Row.names
dd$Row.names = NULL
# library(tidyverse)
# ggtree绘图 #----
p = ggtree::ggtree(hc) %<+% dd +
geom_tippoint(size=5, shape=21, aes(fill= Group, x=x)) +
geom_tiplab(aes(color = Group,x=x * 1.2), hjust=1)
p
# 按照分类学门(j)合并
psdata = ggClusterNet::tax_glom_wt(ps = ps1_rela,ranks = j)
# 转化丰度值
if (tran == TRUE) {
psdata = psdata %>% phyloseq::transform_sample_counts(function(x) {x/sum(x)} )
}
#--提取otu和物种注释表格
otu = phyloseq::otu_table(psdata)
tax = phyloseq::tax_table(psdata)
#--按照指定的Top数量进行筛选与合并
for (i in 1:dim(tax)[1]) {
if (row.names(tax)[i] %in% names(sort(rowSums(otu), decreasing = TRUE)[1:Top])) {
tax[i,j] =tax[i,j]
} else {
tax[i,j]= "Other"
}
}
phyloseq::tax_table(psdata)= tax
##转化为表格
Taxonomies <- psdata %>%
phyloseq::psmelt()
head(Taxonomies) Taxonomies$Abundance = Taxonomies$Abundance * 100
Taxonomies$OTU = NULL
colnames(Taxonomies)[1] = "id"
head(Taxonomies)
p <- p + ggnewscale::new_scale_fill()
p
p1 <- facet_plot(p, panel = 'Stacked Barplot', data = Taxonomies, geom = ggstance::geom_barh,mapping = aes(x = Abundance, fill = !!sym(j)),color = "black",stat='identity' )
p1
grotax <- Taxonomies %>%
dplyr::group_by(Group,!!sym(j)) %>%
dplyr::summarise(Abundance = sum(Abundance))
head(grotax)
data = c()
i = 2
for (i in 1:length(unique(phyloseq::sample_data(psdata)$Group))) {
a <- as.data.frame(table(phyloseq::sample_data(psdata)$Group))[i,1]
b = as.data.frame(table(phyloseq::sample_data(psdata)$Group))[i,2]
c <- grotax %>%
dplyr::filter(Group == a)
c$Abundance <- c$Abundance/b
head(c)
# data = data.frame(Sample =c$Sample,Abundance = c$Abundance,aa =c$aa,Group = c$Group)
data = c
if (i == 1) {
table = data
}
if (i != 1) {
table = rbind(table,data)
}
}
sum( grotax$Abundance)
#> [1] 1800
head(table)
#--绘制分组的聚类结果
ps1_rela = phyloseq::transform_sample_counts(ps, function(x) x / sum(x) )
#按照分组合并OTU表格
otu = as.data.frame((ggClusterNet::vegan_otu(ps1_rela)))
iris.split <- split(otu,as.factor(phyloseq::sample_data(ps1_rela)$Group))
iris.apply <- lapply(iris.split,function(x)colMeans(x))
# 组合结果
iris.combine <- do.call(rbind,iris.apply)
otuG = t(iris.combine)
ps = phyloseq::phyloseq(phyloseq::otu_table(otuG,taxa_are_rows = T),
phyloseq::tax_table(ps1_rela)
)
hc = ps %>%
phyloseq::distance(method = dist) %>%
stats::hclust( method = hcluter_method )
# take grouping with hcluster tree
clus <- cutree(hc, cuttree )
# 提取树中分组的标签和分组编号
d = data.frame(label = names(clus),
member = factor(clus))
# eatract mapping file
map = data.frame(ID = unique(phyloseq::sample_data(ps1_rela)$Group),row.names = unique(phyloseq::sample_data(ps1_rela)$Group),Group = unique(phyloseq::sample_data(ps1_rela)$Group))
# 合并树信息到样本元数据
dd = merge(d,map,by = "row.names",all = F)
row.names(dd) = dd$Row.names
dd$Row.names = NULL
# ggtree绘图 #----
p3 = ggtree(hc) %<+% dd +
geom_tippoint(size=5, shape=21, aes(fill= member, x=x)) +
geom_tiplab(aes(color = member,x=x * 1.2), hjust=1)
p3
p3 <- p3 + ggnewscale::new_scale_fill()
head(grotax)
p4 <- facet_plot(p3, panel = 'Stacked Barplot', data = table, geom = ggstance::geom_barh,mapping = aes(x = Abundance, fill = !!sym(j)),color = "black",stat='identity' )
p4
# return(list(p,p1,p3,p4,Taxonomies))
p5_1 <- p
p5_2 <- p1
p5_3 <- p3
p5_4 <- p4
clubardata <- Taxonomies
ps = readRDS("./data/dataNEW/ps_16s.rds")
FileName1 <- paste(barpath,"/a2_",j,"_cluster_sample",".pdf", sep = "")
ggsave(FileName1, p5_1, width = 6, height = dim(phyloseq::sample_data(ps))[1]/4,limitsize = FALSE)
FileName1 <- paste(barpath,"/a2_",j,"_cluster_bar_sample",".pdf", sep = "")
ggsave(FileName1, p5_2, width = 12, height = dim(phyloseq::sample_data(ps))[1]/4 ,limitsize = FALSE)
FileName1 <- paste(barpath,"/a2_",j,"_cluster_sample",".jpg", sep = "")
ggsave(FileName1, p5_1, width = 6, height =dim(phyloseq::sample_data(ps))[1]/4 ,limitsize = FALSE)
FileName1 <- paste(barpath,"/a2_",j,"_cluster_bar_sample",".jpg", sep = "")
ggsave(FileName1, p5_2, width = 12, height =dim(phyloseq::sample_data(ps))[1]/4 ,limitsize = FALSE)
FileName1 <- paste(barpath,"/a2_",j,"_cluster_Group",".pdf", sep = "")
ggsave(FileName1, p5_3, width = 6, height = gnum,limitsize = FALSE)
FileName1 <- paste(barpath,"/a2_",j,"_cluster_bar_Group",".pdf", sep = "")
ggsave(FileName1, p5_4, width = 12, height = gnum ,limitsize = FALSE)
FileName1 <- paste(barpath,"/a2_",j,"_cluster_Group",".jpg", sep = "")
ggsave(FileName1, p5_3, width = 6, height = gnum ,limitsize = FALSE)
FileName1 <- paste(barpath,"/a2_",j,"_cluster_bar_Group",".jpg", sep = "")
ggsave(FileName1, p5_4, width = 12, height = gnum ,limitsize = FALSE)
FileName <- paste(barpath,"/a2_",j,"_cluster_bar_data",".csv", sep = "")
write.csv(clubardata,FileName)Ternary diagram（三元图）
library(ggtern)
ternpath = paste(otupath,"/ggtern/",sep = "")
dir.create(ternpath)
ps = readRDS("./data/dataNEW/ps_16s.rds")
ternpath = ternpath
ps_rela = phyloseq::transform_sample_counts(ps, function(x) x / sum(x) );ps_rela
#> phyloseq-class experiment-level object
#> otu_table() OTU Table: [ 37178 taxa and 18 samples ]
#> sample_data() Sample Data: [ 18 samples by 2 sample variables ]
#> tax_table() Taxonomy Table: [ 37178 taxa by 7 taxonomic ranks ]
#> phy_tree() Phylogenetic Tree: [ 37178 tips and 37176 internal nodes ]
#> refseq() DNAStringSet: [ 37178 reference sequences ]
otu = ggClusterNet::vegan_otu(ps_rela) %>% as.data.frame()
#数据分组
iris.split <- split(otu,as.factor(as.factor(phyloseq::sample_data(ps)$Group)))
#数据分组计算平均值
iris.apply <- lapply(iris.split,function(x)colSums(x[]))
# 组合结果
iris.combine <- do.call(rbind,iris.apply)
ven2 = t(iris.combine) %>% as.data.frame()
head(ven2) A <- combn(colnames(ven2),3)
ven2$mean = rowMeans(ven2)
tax = ggClusterNet::vegan_tax(ps)
otutax = cbind(ven2,tax)
head(otutax) otutax$Phylum[otutax$Phylum == ""] = "Unknown"
# i = 1
for (i in 1:dim(A)[2]) {
x = A[1,i]
y = A[2,i]
z = A[3,i]
p <- ggtern::ggtern(data=otutax,aes_string(x = x,y=y,z=z,color = "Phylum",size ="mean" ))+geom_point() + theme_void()
p
filename = paste(ternpath,"/",paste(x,y,z,sep = "_"),"_OTU.pdf",sep = "")
ggsave(filename,p,width = 12,height = 10)
filename = paste(ternpath,"/",paste(x,y,z,sep = "_"),"_OTU.png",sep = "")
ggsave(filename,p,width = 12,height = 10,dpi = 100)
filename = paste(ternpath,"/",paste(x,y,z,sep = "_"),"_OTU.csv",sep = "")
write.csv(otutax,filename,quote = FALSE)
}
detach("package:ggtern")Total microbe + Specific microbe (共有微生物+特有微生物)
flowpath = paste(otupath,"/flowplot/",sep = "")
dir.create(flowpath)
group="Group"
#rep=6
m1=2
start=1
a=0.2
b=1
lab.leaf=1
col.cir="yellow"
a.cir=0.5
b.cir=0.5
m1.cir=2
N=0.5
mapping = as.data.frame(phyloseq::sample_data(ps))
aa = ggClusterNet::vegan_otu(ps)
otu_table = as.data.frame(t(aa))
count = aa
sub_design <- as.data.frame(phyloseq::sample_data(ps))
sub_design$SampleType = sub_design$Group
phyloseq::sample_data(ps ) = sub_design
count[count > 0] <- 1
count2 = as.data.frame(count)
# group
iris.split <- split(count2,as.factor(sub_design$Group))
#group mean
iris.apply <- lapply(iris.split,function(x)colSums(x[]))
# conbine result
iris.combine <- do.call(rbind,iris.apply)
ven2 = t(iris.combine)
for (i in 1:length(unique(phyloseq::sample_data(ps)$Group))) {
aa <- as.data.frame(table(phyloseq::sample_data(ps)$Group))[i,1]
bb = as.data.frame(table(phyloseq::sample_data(ps)$Group))[i,2]
ven2[,aa] = ven2[,aa]/bb
}
ven2[ven2 < N] = 0
ven2[ven2 >=N] = 1
ven2 = as.data.frame(ven2)
ven3 = as.list(ven2)
ven2 = as.data.frame(ven2)
all_num = dim(ven2[rowSums(ven2) == length(levels(sub_design$Group)),])[1]
ven2[,1] == 1
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#> [7153] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE
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#> [7213] FALSE FALSE FALSE FALSE TRUE FALSE TRUE TRUE FALSE FALSE FALSE TRUE
#> [7225] FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE TRUE
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#> [21793] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE
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#> [21817] FALSE TRUE FALSE TRUE TRUE FALSE FALSE TRUE FALSE FALSE FALSE TRUE
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#> [21841] FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [21853] TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE
#> [21865] FALSE TRUE TRUE TRUE FALSE FALSE FALSE FALSE FALSE TRUE TRUE TRUE
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#> [21889] FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE TRUE FALSE TRUE
#> [21901] FALSE FALSE FALSE TRUE FALSE TRUE TRUE FALSE FALSE TRUE FALSE FALSE
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#> [21925] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
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#> [21961] FALSE FALSE TRUE FALSE FALSE FALSE FALSE TRUE FALSE FALSE TRUE TRUE
#> [21973] FALSE FALSE FALSE FALSE TRUE TRUE TRUE FALSE FALSE FALSE FALSE FALSE
#> [21985] FALSE FALSE TRUE FALSE FALSE FALSE TRUE TRUE FALSE TRUE FALSE TRUE
#> [21997] FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE TRUE TRUE FALSE FALSE
#> [22009] FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE
#> [22021] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE
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#> [24901] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE TRUE
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#> [24973] TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE
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#> [25069] TRUE TRUE TRUE TRUE FALSE FALSE TRUE FALSE FALSE FALSE TRUE TRUE
#> [25081] TRUE TRUE TRUE TRUE TRUE FALSE TRUE TRUE TRUE TRUE TRUE TRUE
#> [25093] FALSE FALSE TRUE TRUE FALSE TRUE TRUE FALSE FALSE FALSE TRUE TRUE
#> [25105] TRUE FALSE TRUE FALSE FALSE TRUE TRUE TRUE TRUE TRUE FALSE TRUE
#> [25117] FALSE TRUE FALSE FALSE TRUE TRUE TRUE TRUE FALSE TRUE TRUE TRUE
#> [25129] TRUE FALSE TRUE TRUE TRUE TRUE FALSE TRUE FALSE FALSE FALSE FALSE
#> [25141] FALSE FALSE FALSE TRUE TRUE TRUE FALSE TRUE FALSE FALSE TRUE TRUE
#> [25153] TRUE FALSE FALSE FALSE TRUE FALSE FALSE TRUE TRUE TRUE TRUE TRUE
#> [25165] TRUE TRUE TRUE TRUE FALSE FALSE FALSE TRUE FALSE FALSE FALSE TRUE
#> [25177] TRUE TRUE TRUE TRUE FALSE TRUE TRUE FALSE TRUE TRUE TRUE TRUE
#> [25189] TRUE TRUE TRUE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE
#> [25201] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE
#> [25213] FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE TRUE FALSE TRUE FALSE
#> [25225] FALSE FALSE FALSE FALSE FALSE TRUE TRUE TRUE TRUE TRUE FALSE TRUE
#> [25237] FALSE FALSE TRUE TRUE FALSE FALSE FALSE TRUE TRUE TRUE TRUE TRUE
#> [25249] FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE TRUE FALSE TRUE
#> [25261] FALSE TRUE TRUE FALSE TRUE FALSE FALSE TRUE TRUE FALSE TRUE FALSE
#> [25273] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [25285] TRUE FALSE FALSE FALSE FALSE TRUE TRUE TRUE FALSE TRUE FALSE FALSE
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#> [25309] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE
#> [25321] TRUE TRUE TRUE FALSE TRUE FALSE TRUE TRUE FALSE TRUE FALSE FALSE
#> [25333] TRUE FALSE FALSE FALSE TRUE TRUE TRUE TRUE FALSE FALSE FALSE TRUE
#> [25345] FALSE TRUE TRUE TRUE TRUE FALSE TRUE FALSE FALSE FALSE FALSE FALSE
#> [25357] FALSE FALSE TRUE TRUE TRUE TRUE TRUE FALSE FALSE FALSE TRUE FALSE
#> [25369] TRUE FALSE FALSE TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [25381] FALSE FALSE FALSE FALSE TRUE FALSE TRUE FALSE FALSE FALSE TRUE TRUE
#> [25393] TRUE TRUE FALSE FALSE FALSE TRUE TRUE TRUE TRUE FALSE TRUE TRUE
#> [25405] TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE
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#> [25429] FALSE FALSE FALSE FALSE FALSE TRUE TRUE FALSE TRUE TRUE FALSE FALSE
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#> [25465] FALSE TRUE TRUE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE TRUE
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#> [25489] FALSE FALSE TRUE FALSE FALSE FALSE TRUE FALSE FALSE FALSE TRUE TRUE
#> [25501] TRUE TRUE TRUE TRUE FALSE TRUE TRUE TRUE TRUE FALSE FALSE TRUE
#> [25513] FALSE TRUE FALSE TRUE FALSE FALSE FALSE TRUE FALSE FALSE TRUE FALSE
#> [25525] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE
#> [25537] FALSE FALSE FALSE FALSE TRUE FALSE TRUE TRUE TRUE TRUE FALSE FALSE
#> [25549] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE
#> [25561] FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE TRUE FALSE FALSE TRUE
#> [25573] FALSE FALSE FALSE FALSE FALSE FALSE TRUE TRUE TRUE TRUE FALSE TRUE
#> [25585] FALSE FALSE TRUE TRUE TRUE TRUE TRUE FALSE FALSE FALSE TRUE TRUE
#> [25597] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE
#> [25609] FALSE FALSE TRUE TRUE TRUE TRUE TRUE FALSE TRUE FALSE TRUE TRUE
#> [25621] TRUE FALSE TRUE FALSE TRUE TRUE TRUE TRUE TRUE FALSE FALSE TRUE
#> [25633] TRUE TRUE TRUE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE
#> [25645] FALSE FALSE TRUE FALSE TRUE FALSE FALSE FALSE TRUE FALSE FALSE FALSE
#> [25657] FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE TRUE FALSE TRUE
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#> [25693] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
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#> [25741] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [25753] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
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#> [25801] FALSE TRUE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE
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#> [25825] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE
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#> [25849] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [25861] FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [25873] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [25885] FALSE FALSE FALSE FALSE FALSE TRUE TRUE FALSE FALSE FALSE FALSE FALSE
#> [25897] TRUE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [25909] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE TRUE FALSE
#> [25921] FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE
#> [25933] FALSE TRUE FALSE FALSE TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE
#> [25945] FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [25957] FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
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#> [25981] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE
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#> [26005] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [26017] FALSE FALSE TRUE FALSE FALSE FALSE TRUE TRUE FALSE FALSE FALSE TRUE
#> [26029] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE TRUE FALSE
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#> [26053] TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [26065] FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [26077] FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE
#> [26089] FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE TRUE
#> [26101] TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE
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#> [26125] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE
#> [26137] TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
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#> [26185] FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [26197] FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE
#> [26209] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [26221] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [26233] FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE TRUE
#> [26245] FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE
#> [26257] TRUE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE
#> [26269] FALSE TRUE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE TRUE
#> [26281] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [26293] FALSE FALSE TRUE FALSE FALSE TRUE TRUE TRUE FALSE TRUE TRUE TRUE
#> [26305] TRUE TRUE FALSE FALSE FALSE FALSE FALSE TRUE FALSE TRUE TRUE FALSE
#> [26317] FALSE FALSE FALSE FALSE FALSE TRUE FALSE TRUE FALSE FALSE FALSE FALSE
#> [26329] FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE TRUE TRUE FALSE FALSE
#> [26341] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE
#> [26353] FALSE FALSE FALSE FALSE FALSE TRUE FALSE TRUE FALSE FALSE FALSE TRUE
#> [26365] TRUE TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [26377] FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE TRUE TRUE
#> [26389] TRUE TRUE FALSE TRUE FALSE TRUE TRUE TRUE TRUE TRUE FALSE TRUE
#> [26401] FALSE FALSE FALSE FALSE TRUE FALSE TRUE FALSE TRUE TRUE TRUE FALSE
#> [26413] TRUE FALSE FALSE FALSE FALSE TRUE TRUE TRUE FALSE FALSE FALSE FALSE
#> [26425] FALSE TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [26437] TRUE FALSE FALSE TRUE FALSE FALSE TRUE TRUE FALSE FALSE FALSE FALSE
#> [26449] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [26461] FALSE TRUE FALSE FALSE FALSE FALSE TRUE FALSE FALSE TRUE FALSE FALSE
#> [26473] FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE
#> [26485] FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE TRUE
#> [26497] TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [26509] TRUE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE TRUE TRUE
#> [26521] FALSE FALSE TRUE FALSE TRUE FALSE FALSE FALSE FALSE FALSE TRUE FALSE
#> [26533] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [26545] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [26557] FALSE TRUE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE TRUE
#> [26569] FALSE FALSE FALSE FALSE FALSE TRUE FALSE TRUE TRUE FALSE FALSE FALSE
#> [26581] FALSE TRUE FALSE FALSE FALSE FALSE TRUE FALSE FALSE TRUE FALSE TRUE
#> [26593] FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE TRUE FALSE FALSE FALSE
#> [26605] FALSE FALSE FALSE FALSE TRUE FALSE TRUE FALSE FALSE FALSE FALSE TRUE
#> [26617] FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE TRUE FALSE TRUE
#> [26629] TRUE TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE TRUE TRUE TRUE
#> [26641] TRUE TRUE TRUE FALSE FALSE FALSE FALSE TRUE TRUE TRUE TRUE TRUE
#> [26653] TRUE FALSE FALSE TRUE FALSE TRUE TRUE TRUE TRUE TRUE FALSE TRUE
#> [26665] TRUE FALSE TRUE FALSE TRUE TRUE TRUE TRUE TRUE TRUE FALSE TRUE
#> [26677] TRUE TRUE TRUE FALSE TRUE TRUE TRUE FALSE TRUE TRUE TRUE TRUE
#> [26689] TRUE TRUE TRUE TRUE FALSE FALSE FALSE FALSE FALSE TRUE TRUE TRUE
#> [26701] TRUE TRUE TRUE FALSE FALSE FALSE FALSE TRUE FALSE TRUE FALSE FALSE
#> [26713] FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [26725] FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE
#> [26737] FALSE FALSE FALSE TRUE FALSE FALSE FALSE TRUE FALSE TRUE FALSE FALSE
#> [26749] FALSE FALSE FALSE FALSE TRUE TRUE TRUE FALSE TRUE TRUE FALSE TRUE
#> [26761] TRUE TRUE TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE TRUE TRUE
#> [26773] FALSE FALSE FALSE FALSE TRUE TRUE FALSE FALSE TRUE FALSE TRUE FALSE
#> [26785] FALSE FALSE TRUE TRUE FALSE TRUE FALSE FALSE TRUE TRUE FALSE FALSE
#> [26797] FALSE FALSE FALSE TRUE FALSE FALSE FALSE TRUE FALSE TRUE TRUE TRUE
#> [26809] FALSE TRUE FALSE FALSE TRUE FALSE TRUE TRUE FALSE FALSE FALSE TRUE
#> [26821] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE TRUE
#> [26833] FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE
#> [26845] FALSE TRUE TRUE TRUE FALSE FALSE FALSE FALSE FALSE TRUE TRUE FALSE
#> [26857] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [26869] FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE TRUE FALSE
#> [26881] FALSE FALSE FALSE TRUE TRUE FALSE FALSE FALSE FALSE TRUE FALSE FALSE
#> [26893] FALSE FALSE TRUE FALSE FALSE FALSE TRUE FALSE TRUE FALSE FALSE TRUE
#> [26905] FALSE TRUE FALSE TRUE FALSE TRUE FALSE TRUE FALSE TRUE FALSE TRUE
#> [26917] FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
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#> [30925] FALSE FALSE FALSE FALSE TRUE TRUE TRUE FALSE TRUE TRUE TRUE FALSE
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#> [31009] FALSE FALSE FALSE TRUE FALSE TRUE TRUE FALSE FALSE FALSE FALSE TRUE
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#> [31105] FALSE FALSE FALSE FALSE TRUE TRUE TRUE TRUE TRUE FALSE FALSE TRUE
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#> [31129] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE TRUE
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#> [31153] FALSE FALSE TRUE FALSE FALSE TRUE TRUE TRUE FALSE FALSE FALSE TRUE
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#> [31201] FALSE FALSE FALSE TRUE FALSE TRUE FALSE FALSE FALSE FALSE FALSE TRUE
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#> [31285] FALSE FALSE FALSE FALSE TRUE FALSE TRUE TRUE TRUE FALSE FALSE FALSE
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#> [31309] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE TRUE TRUE
#> [31321] FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE TRUE TRUE FALSE TRUE
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#> [31357] FALSE TRUE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE TRUE TRUE
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#> [31465] FALSE TRUE TRUE TRUE TRUE TRUE TRUE FALSE TRUE TRUE TRUE TRUE
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#> [31489] FALSE FALSE FALSE TRUE FALSE TRUE TRUE FALSE TRUE TRUE FALSE FALSE
#> [31501] FALSE FALSE TRUE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE TRUE
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#> [31561] TRUE TRUE TRUE TRUE FALSE TRUE FALSE FALSE FALSE FALSE TRUE FALSE
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#> [31657] FALSE FALSE FALSE FALSE FALSE FALSE TRUE TRUE FALSE FALSE FALSE FALSE
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#> [31681] FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [31693] FALSE FALSE FALSE FALSE TRUE FALSE FALSE TRUE TRUE TRUE FALSE FALSE
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#> [31717] FALSE FALSE TRUE TRUE FALSE TRUE FALSE FALSE FALSE TRUE FALSE FALSE
#> [31729] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE
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#> [31765] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE
#> [31777] FALSE TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [31789] FALSE FALSE FALSE FALSE TRUE FALSE FALSE TRUE TRUE FALSE FALSE FALSE
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#> [36541] FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE TRUE
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#> [36577] FALSE FALSE TRUE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE TRUE
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#> [36625] FALSE FALSE TRUE FALSE FALSE TRUE TRUE FALSE TRUE TRUE FALSE FALSE
#> [36637] FALSE TRUE TRUE FALSE FALSE FALSE TRUE FALSE TRUE TRUE FALSE TRUE
#> [36649] TRUE TRUE FALSE TRUE TRUE FALSE FALSE TRUE FALSE TRUE TRUE FALSE
#> [36661] TRUE FALSE FALSE FALSE TRUE FALSE TRUE TRUE FALSE FALSE FALSE FALSE
#> [36673] TRUE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE TRUE
#> [36685] FALSE TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [36697] FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE TRUE FALSE TRUE FALSE
#> [36709] FALSE FALSE FALSE TRUE TRUE TRUE TRUE TRUE FALSE FALSE TRUE TRUE
#> [36721] TRUE TRUE TRUE TRUE FALSE TRUE TRUE TRUE FALSE TRUE TRUE FALSE
#> [36733] FALSE TRUE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE TRUE TRUE
#> [36745] FALSE TRUE FALSE FALSE TRUE FALSE TRUE TRUE TRUE TRUE FALSE TRUE
#> [36757] TRUE TRUE TRUE TRUE FALSE TRUE TRUE TRUE TRUE TRUE FALSE FALSE
#> [36769] FALSE FALSE TRUE TRUE TRUE TRUE FALSE FALSE FALSE FALSE TRUE TRUE
#> [36781] TRUE FALSE TRUE TRUE TRUE FALSE TRUE TRUE FALSE FALSE FALSE TRUE
#> [36793] FALSE TRUE FALSE FALSE TRUE FALSE FALSE FALSE TRUE TRUE TRUE TRUE
#> [36805] TRUE TRUE TRUE FALSE TRUE TRUE FALSE TRUE FALSE TRUE TRUE TRUE
#> [36817] FALSE FALSE TRUE TRUE TRUE TRUE TRUE FALSE FALSE FALSE FALSE TRUE
#> [36829] TRUE TRUE TRUE TRUE TRUE FALSE TRUE TRUE TRUE FALSE FALSE FALSE
#> [36841] FALSE TRUE FALSE TRUE TRUE FALSE FALSE FALSE FALSE TRUE TRUE FALSE
#> [36853] TRUE FALSE TRUE TRUE TRUE TRUE TRUE FALSE TRUE FALSE FALSE FALSE
#> [36865] FALSE FALSE FALSE FALSE TRUE FALSE TRUE FALSE FALSE FALSE TRUE TRUE
#> [36877] TRUE FALSE TRUE FALSE FALSE FALSE TRUE TRUE TRUE FALSE TRUE FALSE
#> [36889] FALSE FALSE TRUE TRUE TRUE FALSE TRUE FALSE FALSE FALSE TRUE FALSE
#> [36901] TRUE FALSE TRUE TRUE FALSE FALSE TRUE TRUE FALSE FALSE FALSE TRUE
#> [36913] FALSE TRUE FALSE FALSE FALSE TRUE TRUE TRUE FALSE FALSE FALSE TRUE
#> [36925] TRUE TRUE FALSE FALSE FALSE TRUE FALSE FALSE TRUE FALSE FALSE FALSE
#> [36937] TRUE TRUE FALSE FALSE FALSE TRUE FALSE FALSE TRUE FALSE FALSE FALSE
#> [36949] FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE
#> [36961] FALSE TRUE TRUE TRUE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE
#> [36973] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE TRUE
#> [36985] FALSE FALSE FALSE TRUE FALSE TRUE TRUE FALSE TRUE FALSE TRUE FALSE
#> [36997] TRUE TRUE FALSE TRUE TRUE FALSE FALSE TRUE FALSE TRUE TRUE FALSE
#> [37009] FALSE TRUE FALSE FALSE FALSE TRUE FALSE FALSE TRUE FALSE FALSE TRUE
#> [37021] FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE
#> [37033] FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE TRUE FALSE TRUE
#> [37045] TRUE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE
#> [37057] FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE FALSE
#> [37069] FALSE FALSE TRUE TRUE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE
#> [37081] FALSE FALSE FALSE FALSE FALSE TRUE FALSE TRUE FALSE FALSE FALSE FALSE
#> [37093] FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE TRUE TRUE FALSE TRUE
#> [37105] TRUE TRUE TRUE FALSE TRUE TRUE TRUE FALSE TRUE TRUE TRUE FALSE
#> [37117] FALSE TRUE TRUE FALSE FALSE TRUE FALSE FALSE FALSE TRUE FALSE TRUE
#> [37129] TRUE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE FALSE TRUE
#> [37141] FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [37153] FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE
#> [37165] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [37177] FALSE FALSE
A = rep("A",length(colnames(ven2)))
B = rep(1,length(colnames(ven2)))
i = 1
for (i in 1:length(colnames(ven2))) {
B[i] = length(ven2[rowSums(ven2) == 1,][,i][ven2[rowSums(ven2) == 1,][,i] == 1])
A[i] = colnames(ven2)[i]
}
n <- length(A)
deg <- 360 / n
t = 1:n
print(deg)
#> [1] 120
p <- ggplot() +
# geom_point(aes(x = 5 + cos((start + deg * (t - 1)) * pi / 180) * lab.leaf, y = 5 + sin((start + deg * (t - 1)) * pi / 180) *lab.leaf)) +
ggforce::geom_ellipse(aes(x0 = 5 + cos((start + deg * (t - 1)) * pi / 180),
y0 = 5 + sin((start + deg * (t - 1)) * pi / 180),
a = a,
b = b,
angle = (n/2 +seq(0,1000,2)[1:n])/n * pi,
m1 = m1,
fill = as.factor(1:n)),show.legend = F) +
ggforce::geom_ellipse(aes(x0 = 5,y0 = 5,a = a.cir,b = b.cir,angle = 0,m1 = m1.cir),fill = col.cir) +
geom_text(aes(x = 5,y = 5,label = paste("OVER :",all_num,sep = ""))) +
geom_text(aes(
x = 5 + cos((start + deg * (t - 1)) * pi / 180) * lab.leaf,
y = 5 + sin((start + deg * (t - 1)) * pi / 180) * lab.leaf,
label = paste(A,":",B,sep = "")),angle = 360/n*((1:n)-1) ) +
coord_fixed() + theme_void()
p0_2 = p
FileName1 <- paste(flowpath,"ggflowerGroup.pdf", sep = "")
ggsave(FileName1, p0_2, width = 14, height = 14)
FileName2 <- paste(flowpath,"ggflowerGroup.jpg", sep = "")
ggsave(FileName2, p0_2, width = 14, height = 14 )Venn diagram（维恩图）
Venpath = paste(otupath,"/Ven_Upset_super/",sep = "")
dir.create(Venpath)
group = "Group"
path = path
N = 0.5
aa = ggClusterNet::vegan_otu(ps)
otu_table = as.data.frame(t(aa))
count = aa
countA = count
sub_design <- as.data.frame(phyloseq::sample_data(ps))
#
# pick_val_num <- rep/2
count[count > 0] <- 1
count2 = as.data.frame(count )
aa = sub_design[,"Group"]
colnames(aa) = "Vengroup"
#-div group
iris.split <- split(count2,as.factor(aa$Vengroup))
#数据分组计算平均值
iris.apply <- lapply(iris.split,function(x)colSums(x[]))
# 组合结果
iris.combine <- do.call(rbind,iris.apply)
ven2 = t(iris.combine)
for (i in 1:length(unique(phyloseq::sample_data(ps)[,"Group"]))) {
aa <- as.data.frame(table(phyloseq::sample_data(ps)[,"Group"]))[i,1]
bb = as.data.frame(table(phyloseq::sample_data(ps)[,"Group"]))[i,2]
ven2[,aa] = ven2[,aa]/bb
}
ven2[ven2 < N] = 0
ven2[ven2 >=N] = 1
ven2 = as.data.frame(ven2)
#
ven3 = as.list(ven2)
# ven_pick = get.venn.partitions(ven3)
for (i in 1:ncol(ven2)) {
ven3[[i]] <- row.names(ven2[ven2[i] == 1,])
}
if (length(names(ven3)) == 2) {
filename3 = paste(path,"ven_",paste(names(ven3),sep = "",collapse="-"),".pdf",sep = "",collapse="_")
pdf(file=filename3,width = 12, height = 8)
a<- VennDiagram::venn.diagram(ven3,
filename=NULL,
lwd=2,#圈线粗度
lty=1, #圈线类型
fill=c('red',"blue"), #填充颜色
col=c('red',"blue"), #圈线颜色
cat.col=c('red',"blue"),#A和B的颜色
cat.cex = 4,# A和B的大小
rotation.degree = 0,#旋转角度
main = "",#主标题内容
main.cex = 2,#主标题大小
sub = "",#亚标题内容
sub.cex = 1,#亚标题字大小
cex=3,#里面交集字的大小
alpha = 0.5,#透明度
reverse=TRUE,
scaled = FALSE)
grid.draw(a)
dev.off()
filename33 = paste(path,"ven",".jpg",sep = "",collapse="_")
jpeg(file=filename33)
grid.draw(a)
dev.off();
} else if (length(names(ven3)) == 3) {
filename3 = paste(path,"ven_",paste(names(ven3),sep = "",collapse="-"),".pdf",sep = "",collapse="_")
pdf(file=filename3,width = 18, height = 15)
a<- VennDiagram::venn.diagram(ven3,
filename=NULL,
lwd=2,#圈线粗度
lty=1, #圈线类型
fill=c('red',"blue","yellow"), #填充颜色
col=c('red',"blue","yellow"), #圈线颜色
cat.col=c('red',"blue","yellow"),#A和B的颜色
cat.cex = 4,# A和B的大小
rotation.degree = 0,#旋转角度
main = "",#主标题内容
main.cex = 2,#主标题大小
sub = "",#亚标题内容
sub.cex = 1,#亚标题字大小
cex=3,#里面交集字的大小
alpha = 0.5,#透明度
reverse=TRUE,
scaled = FALSE)
grid::grid.draw(a)
dev.off()
filename33 = paste(path,"ven",".jpg",sep = "",collapse="_")
jpeg(file=filename33)
grid::grid.draw(a)
dev.off()
grid::grid.draw(a)
} else if (length(names(ven3)) == 4) {
filename3 = paste(path,"ven_",paste(names(ven3),sep = "",collapse="-"),".pdf",sep = "",collapse="_")
pdf(file=filename3,width = 18, height = 15)
a<-VennDiagram::venn.diagram(ven3,
filename=NULL,
lwd=2,#圈线粗度
lty=1, #圈线类型
fill=c('red',"blue","yellow","#7ad2f6"), #填充颜色
col=c('red',"blue","yellow","#7ad2f6"), #圈线颜色
cat.col=c('red',"blue","yellow","#7ad2f6"),#A和B的颜色
cat.cex = 4,# A和B的大小
rotation.degree = 0,#旋转角度
main = "",#主标题内容
main.cex = 2,#主标题大小
sub = "",#亚标题内容
sub.cex = 1,#亚标题字大小
cex=3,#里面交集字的大小
alpha = 0.5,#透明度
reverse=TRUE,
scaled = FALSE)
grid::grid.draw(a)
dev.off()
filename33 = paste(path,"ven",".jpg",sep = "",collapse="_")
jpeg(file=filename33)
grid::grid.draw(a)
dev.off()
grid::grid.draw(a)
}else if (length(names(ven3)) == 5) {
filename3 = paste(path,"ven_",paste(names(ven3),sep = "",collapse="-"),".pdf",sep = "",collapse="_")
pdf(file=filename3,width = 12, height = 12)
a<- VennDiagram::venn.diagram(ven3,
filename=NULL,
lwd=2,#圈线粗度
lty=1, #圈线类型
fill=c('red',"blue","yellow","#7ad2f6","green"), #填充颜色
col=c('red',"blue","yellow","#7ad2f6","green"), #圈线颜色
cat.col=c('red',"blue","yellow","#7ad2f6","green"),#A和B的颜色
cat.cex = 4,# A和B的大小
rotation.degree = 0,#旋转角度
main = "",#主标题内容
main.cex = 2,#主标题大小
sub = "",#亚标题内容
sub.cex = 1,#亚标题字大小
cex=3,#里面交集字的大小
alpha = 0.5,#透明度
reverse=TRUE,
scaled = FALSE)
grid::grid.draw(a)
dev.off()
filename33 = paste(path,"ven",".jpg",sep = "",collapse="_")
jpeg(file=filename33)
grid::grid.draw(a)
dev.off()
grid::grid.draw(a)
}else if (length(names(ven3)) == 6) {
print("ven not use for more than 6")
}
Venn-network diagram（维恩网络图）
library(ggClusterNet)
library(phyloseq)
biospath = paste(otupath,"/biospr_network_Ven/",sep = "")
dir.create(biospath)
N = 0.5
result = ggClusterNet::div_network(ps)
edge = result[[1]]
data = result[[3]]
result <- ggClusterNet::div_culculate(table = result[[3]],distance = 1.1,distance2 = 1.5,distance3 = 1.3,order = FALSE)
# result <- div_culculate(table = result[[3]],distance = 1,distance2 = 1.2,distance3 = 1.1,order = FALSE)
edge = result[[1]]
plotdata = result[[2]]
#--这部分数据是样本点数据
groupdata <- result[[3]]
# table(plotdata$elements)
node = plotdata[plotdata$elements == unique(plotdata$elements), ]
otu_table = as.data.frame(t(ggClusterNet::vegan_otu(ps)))
tax_table = as.data.frame(ggClusterNet::vegan_tax(ps))
res = merge(node,tax_table,by = "row.names",all = F)
row.names(res) = res$Row.names
res$Row.names = NULL
plotcord = res
xx = data.frame(mean =rowMeans(otu_table))
plotcord = merge(plotcord,xx,by = "row.names",all = FALSE)
head(plotcord)# plotcord$Phylum
row.names(plotcord) = plotcord$Row.names
plotcord$Row.names = NULL
head(plotcord)library(ggrepel)
head(plotcord)head(groupdata)p = ggplot() + geom_segment(aes(x = X1, y = Y1, xend = X2, yend = Y2),
data = edge, size = 0.3,color = "yellow") +
geom_point(aes(X1, X2,fill = Phylum,size =mean ),pch = 21, data = plotcord) +
geom_point(aes(X1, X2),pch = 21, data = groupdata,size = 5,fill = "blue",color = "black") +
geom_text(aes(X1, X2,label = elements ), data = groupdata,hjust = 1,vjust = -1) +
theme_void()
p
filename = paste(biospath,"/","biostr_Ven_network.pdf",sep = "")
ggsave(filename,p,width = (15),height = (12))
filename = paste(biospath,"/","biostr_Ven_network.jpg",sep = "")
ggsave(filename,p,width = (15),height = (12))
detach("package:ggClusterNet")
detach("package:phyloseq")Sankey diagram（桑基图）
# BiocManager::install("networkD3")
# BiocManager::install("webshot")
library(ggClusterNet)
library(phyloseq)
snapath = paste(otupath,"/sankeyNetwork/",sep = "");otupath
#> [1] ".//result_and_plot//Base_diversity_16s/OTU/"
dir.create(snapath)
ps = readRDS("./data/dataNEW/ps_16s.rds")
rank=6#参数目前不可修改
Top=50
snapath=snapath
map = sample_data(ps) %>% as.tibble()
otu = ps %>% vegan_otu() %>%
as.data.frame()
otu$ID = row.names(otu)
tax = ps %>%
scale_micro() %>%
vegan_tax() %>%
as.data.frame()
tax$taxid = row.names(tax)
head(tax)
data = map %>% as.tibble() %>%
inner_join(otu) %>%
gather( taxa, count, starts_with("ASV_")) %>%
inner_join(tax,by = c("taxa" = "taxid") )
data
tax = ps %>%
ggClusterNet::tax_glom_wt(ranks = rank) %>%
ggClusterNet::filter_OTU_ps(Top) %>%
subset_taxa(!Genus %in% c("Unassigned","Unknown")) %>%
ggClusterNet::vegan_tax() %>%
as.data.frame()
head(tax) dim(tax)
#> [1] 49 6
id2 = c("k","p","c","o","f","g")
dat = NULL
for (i in 1:5) {
dat <- tax[,c(i,i+1)] %>% distinct(.keep_all = TRUE)
colnames(dat) = c("source","target")
dat$source = paste(id2[i],dat$source,sep = "_")
dat$target = paste(id2[i+1],dat$target,sep = "_")
if (i == 1) {
dat2 = dat
}
dat2 = rbind(dat2,dat)
}
dim(dat2)
#> [1] 169 2
# dat2 = dat2 %>% distinct(.keep_all = TRUE)
head(dat2)
otu = ps %>%
ggClusterNet::tax_glom_wt(ranks = 6) %>%
ggClusterNet::scale_micro() %>%
ggClusterNet::filter_OTU_ps(Top) %>%
subset_taxa(!Genus %in% c("Unassigned","Unknown")) %>%
ggClusterNet::vegan_otu() %>%
t() %>%
as.data.frame()
head(otu) otutax = cbind(otu,tax)
id = rank.names(ps)[1:6]
dat = NULL
dat3 = NULL
head(data)
for (j in 1) {
dat = data %>% group_by(Group,!!sym(rank_names(ps)[j])) %>%
summarise_if(is.numeric,sum,na.rm = TRUE)
colnames(dat) = c("source","target","value")
dat$target = paste(id2[j],dat$target,sep = "_")
if (j == 1) {
dat3 = dat
}
# dat3 = rbind(dat3,dat)
}
dat3 %>% tail() tem = data.frame(target = dat3$target,value = dat3$value)
dat3$value = NULL
dat2 = rbind(dat2,dat3)
head( otutax) for (i in 1:6) {
dat <- otutax %>%
dplyr::group_by(!!sym(id[i])) %>%
summarise_if(is.numeric,sum,na.rm = TRUE)
dat = data.frame(Genus = dat[,1],value =rowSums(dat[,-1]) )
colnames(dat) = c("target","value")
dat$target = paste(id2[i],dat$target,sep = "_")
if (i == 1) {
dat3 = dat
}
dat3 = rbind(dat3,dat)
}
dat4 <- dat2 %>% left_join(dat3)
sankey = dat4
head( sankey )
nodes <- data.frame(name = unique(c(as.character(sankey$source),as.character(sankey$target))),stringsAsFactors = FALSE)
nodes$ID <- 0:(nrow(nodes)-1)
sankey <- merge(sankey,nodes,by.x = "source",by.y = "name")
sankey <- merge(sankey,nodes,by.x = "target",by.y = "name")
colnames(sankey) <- c("X","Y","value","source","target")
sankey <- subset(sankey,select = c("source","target","value"))
nodes <- subset(nodes,select = c("name"))
ColourScal='d3.scaleOrdinal() .range(["#E41A1C", "#377EB8", "#4DAF4A", "#984EA3", "#FF7F00", "#FFFF33", "#A65628", "#F781BF", "#999999"])'
sankey$energy_type <- sub(' .*', '', nodes[sankey$source + 1, 'name'])
library(networkD3)
p <- sankeyNetwork(Links = sankey, Nodes = nodes,
Source = "source",Target = "target",Value = "value",
NodeID = "name",
sinksRight=FALSE,
LinkGroup = 'energy_type',
colourScale= ColourScal,
# nodeWidth=40,
# fontSize=13
# nodePadding=20
)
# return(list(p,sankey))
dat = sankey
FileName <-paste(snapath,"/sankey_Group.csv", sep = "")
write.csv(dat,FileName,sep = "")
saveNetwork(p,paste(snapath,"/sankey_Group.html", sep = ""))
library(webshot)
# webshot::install_phantomjs()
# webshot(paste(snapath,"/sankey1.html", sep = "") ,paste(snapath,"/sankey1.png", sep = ""))
webshot(paste(snapath,"/sankey_Group.html", sep = "") , paste(snapath,"/sankey_Group.pdf", sep = ""))2.Difference analysis (2.差异分析)
edgeR and DESep2-Volcano plot（edgeR和DESep2-火山图）
ps = readRDS("./data/dataNEW/ps_16s.rds")
diffpath = paste(otupath,"/diff_tax/",sep = "")
dir.create(diffpath)
diffpath.1 = paste(diffpath,"/DEsep2/",sep = "")
dir.create(diffpath.1)
diffpath.2 = diffpath.1
# 准备脚本
group = "Group"
pvalue = 0.05
lfc =0
artGroup = NULL
method = "TMM"
j = 2
path = diffpath
b = NULL
if (j %in% c("OTU","gene","meta")) {
ps = ps
} else if (j %in% c(1:7)) {
ps = ps %>%
ggClusterNet::tax_glom_wt(ranks = j)
} else if (j %in% c("Kingdom","Phylum","Class","Order","Family","Genus","Species")){
} else {
ps = ps
print("unknown j, checked please")
}
sub_design <- as.data.frame(phyloseq::sample_data(ps))
Desep_group <- as.character(levels(as.factor(sub_design$Group)))
Desep_group
#> [1] "Group1" "Group2" "Group3"
if ( is.null(artGroup)) {
#--构造两两组合#-----
aaa = combn(Desep_group,2)
# sub_design <- as.data.frame(sample_data(ps))
}
if (!is.null(artGroup)) {
aaa = as.matrix(b)
}
otu_table = as.data.frame(ggClusterNet::vegan_otu(ps))
count = as.matrix(otu_table)
count <- t(count)
sub_design <- as.data.frame(phyloseq::sample_data(ps))
dim(sub_design)
#> [1] 18 2
sub_design$SampleType = as.character(sub_design$Group)
sub_design$SampleType <- as.factor(sub_design$Group)
# create DGE list
d = edgeR::DGEList(counts=count, group=sub_design$SampleType)
d$samples d = edgeR::calcNormFactors(d,method=method)#默认为TMM标准化
# Building experiment matrix
design.mat = model.matrix(~ 0 + d$samples$group)
colnames(design.mat)=levels(sub_design$SampleType)
d2 = edgeR::estimateGLMCommonDisp(d, design.mat)
d2 = edgeR::estimateGLMTagwiseDisp(d2, design.mat)
fit = edgeR::glmFit(d2, design.mat)
#------------根据分组提取需要的差异结果#------------
for (i in 1:dim(aaa)[2]) {
# i = 1
Desep_group = aaa[,i]
print( Desep_group)
# head(design)
# 设置比较组写在前面的分组为enrich表明第一个分组含量高
# ?limma::makeContrasts
group = paste(Desep_group[1],Desep_group[2],sep = "-")
group
BvsA <- limma::makeContrasts(contrasts = group,levels=c( as.character(levels(as.factor(sub_design$Group)))) )#注意是以GF1为对照做的比较
# 组间比较,统计Fold change, Pvalue
lrt = edgeR::glmLRT(fit,contrast=BvsA)
# FDR检验，控制假阳性率小于5%
de_lrt = edgeR::decideTestsDGE(lrt, adjust.method="fdr", p.value=pvalue,lfc=lfc)#lfc=0这个是默认值
summary(de_lrt)
# 导出计算结果
x=lrt$table
x$sig=de_lrt
head(x)
#------差异结果符合otu表格的顺序
row.names(count)[1:6]
x <- cbind(x, padj = p.adjust(x$PValue, method = "fdr"))
enriched = row.names(subset(x,sig==1))
depleted = row.names(subset(x,sig==-1))
x$level = as.factor(ifelse(as.vector(x$sig) ==1, "enriched",ifelse(as.vector(x$sig)==-1, "depleted","nosig")))
x = data.frame(row.names = row.names(x),logFC = x$logFC,level = x$level,p = x$PValue)
head(x)
# colnames(x) = paste(group,colnames(x),sep = "")
# x = res
# head(x)
#------差异结果符合otu表格的顺序
# x = data.frame(row.names = row.names(x),logFC = x$log2FoldChange,level = x$level,p = x$pvalue)
x1 = x %>%
dplyr::filter(level %in% c("enriched","depleted","nosig") )
head(x1)
x1$Genus = row.names(x1)
# x$level = factor(x$level,levels = c("enriched","depleted","nosig"))
if (nrow(x1)<= 1) {
}
x2 <- x1 %>%
dplyr::mutate(ord = logFC^2) %>%
dplyr::filter(level != "nosig") %>%
dplyr::arrange(desc(ord)) %>%
head(n = 5)
file = paste(path,"/",group,j,"_","Edger_Volcano_Top5.csv",sep = "")
write.csv(x2,file,quote = F)
head(x2)
p <- ggplot(x1,aes(x =logFC ,y = -log2(p), colour=level)) +
geom_point() +
geom_hline(yintercept=-log10(0.2),
linetype=4,
color = 'black',
size = 0.5) +
geom_vline(xintercept=c(-1,1),
linetype=3,
color = 'black',
size = 0.5) +
ggrepel::geom_text_repel(data=x2, aes(x =logFC ,y = -log2(p), label=Genus), size=1) +
scale_color_manual(values = c('blue2','red2', 'gray30')) +
ggtitle(group) + theme_bw()
p
file = paste(path,"/",group,j,"_","Edger_Volcano.pdf",sep = "")
ggsave(file,p,width = 8,height = 6)
file = paste(path,"/",group,j,"_","Edger_Volcano.png",sep = "")
ggsave(file,p,width = 8,height = 6)
colnames(x) = paste(group,colnames(x),sep = "")
if (i ==1) {
table =x
}
if (i != 1) {
table = cbind(table,x)
}
}
#> [1] "Group1" "Group2"
#> [1] "Group1" "Group3"
#> [1] "Group2" "Group3"
x = table
###########添加物种丰度#----------
# dim(count)
# str(count)
count = as.matrix(count)
norm = t(t(count)/colSums(count)) #* 100 # normalization to total 100
dim(norm)
#> [1] 39 18
norm1 = norm %>%
t() %>% as.data.frame()
# head(norm1)
#数据分组计算平均值
library("tidyverse")
head(norm1)
iris.split <- split(norm1,as.factor(sub_design$SampleType))
iris.apply <- lapply(iris.split,function(x)colMeans(x))
# 组合结果
iris.combine <- do.call(rbind,iris.apply)
norm2= t(iris.combine)
#head(norm)
str(norm2)
#> num [1:39, 1:3] 1.56e-01 3.58e-02 2.84e-06 8.69e-03 6.02e-02 ...
#> - attr(*, "dimnames")=List of 2
#> ..$ : chr [1:39] "Acidobacteria" "Actinobacteria" "Aminicenantes" "Armatimonadetes" ...
#> ..$ : chr [1:3] "Group1" "Group2" "Group3"
norm2 = as.data.frame(norm2)
# dim(x)
head(norm2) x = cbind(x,norm2)
head(x) #在加入这个文件taxonomy时，去除后面两列不相干的列
# 读取taxonomy，并添加各列名称
if (!is.null(ps@tax_table)) {
taxonomy = as.data.frame(ggClusterNet::vegan_tax(ps))
head(taxonomy)
# taxonomy <- as.data.frame(tax_table(ps1))
#发现这个注释文件并不适用于直接作图。
#采用excel将其分列处理，并且删去最后一列，才可以运行
if (length(colnames(taxonomy)) == 6) {
colnames(taxonomy) = c("kingdom","phylum","class","order","family","genus")
}else if (length(colnames(taxonomy)) == 7) {
colnames(taxonomy) = c("kingdom","phylum","class","order","family","genus","species")
}else if (length(colnames(taxonomy)) == 8) {
colnames(taxonomy) = c("kingdom","phylum","class","order","family","genus","species","rep")
}
# colnames(taxonomy) = c("kingdom","phylum","class","order","family","genus")
# Taxonomy排序，并筛选OTU表中存在的
library(dplyr)
taxonomy$id=rownames(taxonomy)
# head(taxonomy)
tax = taxonomy[row.names(x),]
x = x[rownames(tax), ] # reorder according to tax
if (length(colnames(taxonomy)) == 7) {
x = x[rownames(tax), ] # reorder according to tax
x$phylum = gsub("","",tax$phylum,perl=TRUE)
x$class = gsub("","",tax$class,perl=TRUE)
x$order = gsub("","",tax$order,perl=TRUE)
x$family = gsub("","",tax$family,perl=TRUE)
x$genus = gsub("","",tax$genus,perl=TRUE)
# x$species = gsub("","",tax$species,perl=TRUE)
}else if (length(colnames(taxonomy)) == 8) {
x$phylum = gsub("","",tax$phylum,perl=TRUE)
x$class = gsub("","",tax$class,perl=TRUE)
x$order = gsub("","",tax$order,perl=TRUE)
x$family = gsub("","",tax$family,perl=TRUE)
x$genus = gsub("","",tax$genus,perl=TRUE)
x$species = gsub("","",tax$species,perl=TRUE)
}else if (length(colnames(taxonomy)) == 9) {
x = x[rownames(tax), ] # reorder according to tax
x$phylum = gsub("","",tax$phylum,perl=TRUE)
x$class = gsub("","",tax$class,perl=TRUE)
x$order = gsub("","",tax$order,perl=TRUE)
x$family = gsub("","",tax$family,perl=TRUE)
x$genus = gsub("","",tax$genus,perl=TRUE)
x$species = gsub("","",tax$species,perl=TRUE)
}
} else {
x = cbind(x,tax)
}
res = x
head(res) filename = paste(diffpath.2,"/","_",j,"_","edger_all.csv",sep = "")
write.csv(res,filename)
j = "Genus"
group = "Group"
pvalue = 0.05
artGroup = NULL
path = diffpath
# ps = ps %>%
# ggClusterNet::tax_glom_wt(ranks = j)
if (j %in% c("OTU","gene","meta")) {
ps = ps
} else if (j %in% c(1:7)) {
ps = ps %>%
ggClusterNet::tax_glom_wt(ranks = j)
} else if (j %in% c("Kingdom","Phylum","Class","Order","Family","Genus","Species")){
} else {
ps = ps
print("unknown j, checked please")
}
#> NULL
Desep_group <- ps %>%
phyloseq::sample_data() %>%
.$Group %>%
as.factor() %>%
levels() %>%
as.character()
if ( is.null(artGroup)) {
#--构造两两组合#-----
aaa = combn(Desep_group,2)
# sub_design <- as.data.frame(sample_data(ps))
} else if (!is.null(artGroup)) {
aaa = as.matrix(b )
}
count <- ps %>%
ggClusterNet::vegan_otu() %>% round(0) %>%
t()
map = ps %>%
phyloseq::sample_data() %>%
as.tibble() %>%
as.data.frame()
dds <- DESeq2::DESeqDataSetFromMatrix(countData = count,
colData = map,
design = ~ Group)
dds2 <- DESeq2::DESeq(dds) ##第二步,标准化
# resultsNames(dds2)
#------------根据分组提取需要的差异结果#------------
for (i in 1:dim(aaa)[2]) {
# i = 1
Desep_group = aaa[,i]
print( Desep_group)
# head(design)
# 设置比较组写在前面的分组为enrich表明第一个分组含量高
group = paste(Desep_group[1],Desep_group[2],sep = "-")
group
# 将结果用results()函数来获取，赋值给res变量
res <- DESeq2::results(dds2, contrast=c("Group",Desep_group ),alpha=0.05)
# 导出计算结果
x = res
head(x)
#------差异结果符合otu表格的顺序
x$level = as.factor(ifelse(as.vector(x$padj) < 0.05 & x$log2FoldChange > 0, "enriched",
ifelse(as.vector(x$padj) < 0.05 &x$log2FoldChange < 0, "depleted","nosig")))
x = data.frame(row.names = row.names(x),logFC = x$log2FoldChange,level = x$level,p = x$pvalue)
x1 = x %>%
filter(level %in% c("enriched","depleted","nosig") )
head(x1)
x1$Genus = row.names(x1)
# x$level = factor(x$level,levels = c("enriched","depleted","nosig"))
x2 <- x1 %>%
dplyr::mutate(ord = logFC^2) %>%
dplyr::filter(level != "nosig") %>%
dplyr::arrange(desc(ord)) %>%
head(n = 5)
file = paste(path,"/",group,j,"_","DESep2_Volcano_Top5.csv",sep = "")
write.csv(x2,file,quote = F)
head(x2)
p <- ggplot(x1,aes(x =logFC ,y = -log2(p), colour=level)) +
geom_point() +
geom_hline(yintercept=-log10(0.2),
linetype=4,
color = 'black',
size = 0.5) +
geom_vline(xintercept=c(-1,1),
linetype=3,
color = 'black',
size = 0.5) +
ggrepel::geom_text_repel(data=x2, aes(x =logFC ,y = -log2(p), label=Genus), size=1) +
scale_color_manual(values = c('blue2','red2', 'gray30')) +
ggtitle(group) + theme_bw()
p
file = paste(path,"/",group,j,"_","DESep2_Volcano.pdf",sep = "")
ggsave(file,p,width = 8,height = 6)
file = paste(path,"/",group,j,"_","DESep2_Volcano.png",sep = "")
ggsave(file,p,width = 8,height = 6)
colnames(x) = paste(group,colnames(x),sep = "")
if (i ==1) {
table =x
}
if (i != 1) {
table = cbind(table,x)
}
}
#> [1] "Group1" "Group2"
#> [1] "Group1" "Group3"
#> [1] "Group2" "Group3"
count = as.matrix(count)
norm = t(t(count)/colSums(count)) #* 100 # normalization to total 100
dim(norm)
#> [1] 39 18
norm1 = norm %>%
t() %>% as.data.frame()
# head(norm1)
#数据分组计算平均值
# library("tidyverse")
head(norm1)
iris.split <- split(norm1,as.factor(map$Group))
iris.apply <- lapply(iris.split,function(x)colMeans(x))
# 组合结果
iris.combine <- do.call(rbind,iris.apply)
norm2= t(iris.combine)
#head(norm)
str(norm2)
#> num [1:39, 1:3] 1.56e-01 3.58e-02 2.84e-06 8.69e-03 6.02e-02 ...
#> - attr(*, "dimnames")=List of 2
#> ..$ : chr [1:39] "Acidobacteria" "Actinobacteria" "Aminicenantes" "Armatimonadetes" ...
#> ..$ : chr [1:3] "Group1" "Group2" "Group3"
norm2 = as.data.frame(norm2)
head(norm2) x = cbind(table,norm2)
head(x) #在加入这个文件taxonomy时，去除后面两列不相干的列
# 读取taxonomy，并添加各列名称
if (!is.null(ps@tax_table)) {
taxonomy = as.data.frame(ggClusterNet::vegan_tax(ps))
head(taxonomy)
# taxonomy <- as.data.frame(tax_table(ps1))
#发现这个注释文件并不适用于直接作图。
#采用excel将其分列处理，并且删去最后一列，才可以运行
if (length(colnames(taxonomy)) == 6) {
colnames(taxonomy) = c("kingdom","phylum","class","order","family","genus")
}else if (length(colnames(taxonomy)) == 7) {
colnames(taxonomy) = c("kingdom","phylum","class","order","family","genus","species")
}else if (length(colnames(taxonomy)) == 8) {
colnames(taxonomy) = c("kingdom","phylum","class","order","family","genus","species","rep")
}
# colnames(taxonomy) = c("kingdom","phylum","class","order","family","genus")
# Taxonomy排序，并筛选OTU表中存在的
library(dplyr)
taxonomy$id=rownames(taxonomy)
# head(taxonomy)
tax = taxonomy[row.names(x),]
x = x[rownames(tax), ] # reorder according to tax
if (length(colnames(taxonomy)) == 7) {
x = x[rownames(tax), ] # reorder according to tax
x$phylum = gsub("","",tax$phylum,perl=TRUE)
x$class = gsub("","",tax$class,perl=TRUE)
x$order = gsub("","",tax$order,perl=TRUE)
x$family = gsub("","",tax$family,perl=TRUE)
x$genus = gsub("","",tax$genus,perl=TRUE)
# x$species = gsub("","",tax$species,perl=TRUE)
}else if (length(colnames(taxonomy)) == 8) {
x$phylum = gsub("","",tax$phylum,perl=TRUE)
x$class = gsub("","",tax$class,perl=TRUE)
x$order = gsub("","",tax$order,perl=TRUE)
x$family = gsub("","",tax$family,perl=TRUE)
x$genus = gsub("","",tax$genus,perl=TRUE)
x$species = gsub("","",tax$species,perl=TRUE)
}else if (length(colnames(taxonomy)) == 9) {
x = x[rownames(tax), ] # reorder according to tax
x$phylum = gsub("","",tax$phylum,perl=TRUE)
x$class = gsub("","",tax$class,perl=TRUE)
x$order = gsub("","",tax$order,perl=TRUE)
x$family = gsub("","",tax$family,perl=TRUE)
x$genus = gsub("","",tax$genus,perl=TRUE)
x$species = gsub("","",tax$species,perl=TRUE)
}
} else {
x = x
}
res = x
head(res)
filename = paste(diffpath.1,"/","_",j,"_","DESep2_all.csv",sep = "")
write.csv(res,filename,quote = F)edgeR-Manhattan diagram（edgeR-曼哈顿图）
diffpath = paste(otupath,"/diff_Manhattan/",sep = "")
dir.create(diffpath)
ps = ps
pvalue = 0.05
lfc = 0
diffpath = diffpath
count = ps %>%
ggClusterNet::vegan_otu() %>%
t()
# create DGE list
d = edgeR::DGEList(counts=count, group= phyloseq::sample_data(ps)$Group)
d = edgeR::calcNormFactors(d)
design.mat = model.matrix(~ 0 + d$samples$group)
colnames(design.mat)=levels(as.factor(phyloseq::sample_data(ps)$Group))
d2 = edgeR::estimateGLMCommonDisp(d, design.mat)
d2 = edgeR::estimateGLMTagwiseDisp(d2, design.mat)
fit = edgeR::glmFit(d2, design.mat)
Desep_group <- as.character(levels(as.factor(phyloseq::sample_data(ps)$Group)))
Desep_group
#> [1] "Group1" "Group2" "Group3"
aaa = combn(Desep_group,2)
for (i in 1:dim(aaa)[2]) {
# i = 1
Desep_group = aaa[,i]
print( Desep_group)
group = paste(Desep_group[1],Desep_group[2],sep = "-")
group
BvsA <- limma::makeContrasts(contrasts = group,levels=design.mat)#注意是以GF1为对照做的比较
# 组间比较,统计Fold change, Pvalue
lrt = edgeR::glmLRT(fit,contrast=BvsA)
# FDR检验，控制假阳性率小于5%
de_lrt = edgeR::decideTestsDGE(lrt, adjust.method="fdr", p.value=pvalue,lfc=lfc)#lfc=0这个是默认值
summary(de_lrt)
# 导出计算结果
x=lrt$table
x$sig=de_lrt
x$sig=de_lrt
head(x)
#------差异结果符合otu表格的顺序
row.names(count)[1:6]
x <- cbind(x, padj = p.adjust(x$PValue, method = "fdr"))
enriched = row.names(subset(x,sig==1))
depleted = row.names(subset(x,sig==-1))
x$level = as.factor(ifelse(as.vector(x$sig) ==1, "enriched",ifelse(as.vector(x$sig)==-1, "depleted","nosig")))
x$otu = rownames(x)
x$neglogp = -log(x$PValue)
tax = ps %>%
ggClusterNet::vegan_tax() %>%
as.data.frame()
head(tax)
x = cbind(x,tax)
head(x)
top_phylum=c("Bacteroidetes","Firmicutes","Planctomycetes","Proteobacteria","Verrucomicrobia")
x[!(x$Phylum %in% top_phylum),]$Phylum = "Low Abundance" # no level can get value
x$otu = factor(x$otu, levels=x$otu) # set x order
x$level = factor(x$level, levels=c("enriched","depleted","nosig"))
levels(x$Phylum)=c(top_phylum,"Low Abundance")
x = x %>% arrange(Phylum)
head(x)
x$otu = factor(x$otu, levels=x$otu)
# if (tem != 0) {
# tem = x[x$neglogp>15,] %>% nrow()
# }
FDR = min(x$neglogp[x$level=="depleted"])
p = ggplot(x, aes(x=otu, y=neglogp, color=Phylum, size=logCPM, shape=level)) +
geom_point(alpha=.7) +
geom_hline(yintercept=FDR, linetype=2, color="lightgrey") +
scale_shape_manual(values=c(17, 25, 20))+
scale_size(breaks=c(5, 10, 15)) +
labs(x="OTU", y="-loge(P)") +
theme(axis.ticks.x=element_blank(),axis.text.x=element_blank(),legend.position="top") +
scale_color_manual(values = c(RColorBrewer::brewer.pal(9,"Set1")))
p
filename = paste(diffpath,"/",paste(Desep_group[1],Desep_group[2],sep = "_"),"Manhattan_plot.pdf",sep = "")
ggsave(filename,p,width = 16,height = 6)
filename = paste(diffpath,"/",paste(Desep_group[1],Desep_group[2],sep = "_"),"Manhattan_plot.png",sep = "")
ggsave(filename,p,width = 16,height = 6,dpi = 72)
}
#> [1] "Group1" "Group2"
#> [1] "Group1" "Group3"
#> [1] "Group2" "Group3"STAMP_difference analysis（STAMP_差异分析）
ps = readRDS("./data/dataNEW/ps_16s.rds")
diffpath = paste(otupath,"/stemp_diff/",sep = "")
dir.create(diffpath)
allgroup <- combn(unique(map$Group),2)
ps_sub <- phyloseq::subset_samples(ps,Group %in% allgroup[,1]);ps_sub
#> phyloseq-class experiment-level object
#> otu_table() OTU Table: [ 37178 taxa and 12 samples ]
#> sample_data() Sample Data: [ 12 samples by 2 sample variables ]
#> tax_table() Taxonomy Table: [ 37178 taxa by 7 taxonomic ranks ]
#> phy_tree() Phylogenetic Tree: [ 37178 tips and 37176 internal nodes ]
#> refseq() DNAStringSet: [ 37178 reference sequences ]
Top = 20
ranks = 6
method = "TMM"
test.method = "t.test"
#--门水平合并
data = ggClusterNet::tax_glom_wt(ps_sub,ranks = phyloseq::rank_names(ps)[ranks]) %>%
ggClusterNet::scale_micro(method = method) %>%
ggClusterNet::filter_OTU_ps(Top = 200) %>%
ggClusterNet::vegan_otu() %>%
as.data.frame()
tem = colnames(data)
data$ID = row.names(data)
data <- data %>%
dplyr::inner_join(as.tibble(phyloseq::sample_data(ps)),by = "ID")
data$Group = as.factor(data$Group)
if (test.method == "t.test") {
diff <- data[,tem] %>%
# dplyr::select_if(is.numeric) %>%
purrr::map_df(~ broom::tidy(t.test(. ~ Group,data = data)), .id = 'var')
} else if(test.method == "wilcox.test"){
diff <- data[,tem] %>%
# dplyr::select_if(is.numeric) %>%
purrr::map_df(~ broom::tidy(wilcox.test(. ~ Group,data = data)), .id = 'var')
}
diff$p.value[is.nan(diff$p.value)] = 1
diff$p.value <- p.adjust(diff$p.value,"bonferroni")
tem = diff$p.value [diff$p.value < 0.05] %>% length()
if (tem > 30) {
diff <- diff %>%
dplyr::filter(p.value < 0.05) %>%
head(30)
} else {
diff <- diff %>%
# filter(p.value < 0.05) %>%
head(30)
}
# diff <- diff %>% filter(p.value < 0.05)
# diff1$p.value <- p.adjust(diff1$p.value,"bonferroni")
# diff1 <- diff1 %>% filter(p.value < 0.05)
abun.bar <- data[,c(diff$var,"Group")] %>%
tidyr::gather(variable,value,-Group) %>%
dplyr::group_by(variable,Group) %>%
dplyr::summarise(Mean = mean(value))
diff.mean <- diff[,c("var","estimate","conf.low","conf.high","p.value")]
diff.mean$Group <- c(ifelse(diff.mean$estimate >0,levels(data$Group)[1],
levels(data$Group)[2]))
diff.mean <- diff.mean[order(diff.mean$estimate,decreasing = TRUE),]
cbbPalette <- c("#E69F00", "#56B4E9")
abun.bar$variable <- factor(abun.bar$variable,levels = rev(diff.mean$var))
p1 <- ggplot(abun.bar,aes(variable,Mean,fill = Group)) +
scale_x_discrete(limits = levels(diff.mean$var)) +
coord_flip() +
xlab("") +
ylab("Mean proportion (%)") +
theme(panel.background = element_rect(fill = 'transparent'),
panel.grid = element_blank(),
axis.ticks.length = unit(0.4,"lines"),
axis.ticks = element_line(color='black'),
axis.line = element_line(colour = "black"),
axis.title.x=element_text(colour='black', size=12,face = "bold"),
axis.text=element_text(colour='black',size=10,face = "bold"),
legend.title=element_blank(),
# legend.text=element_text(size=12,face = "bold",colour = "black",
# margin = margin(r = 20)),
legend.position = c(-1,-0.1),
legend.direction = "horizontal",
legend.key.width = unit(0.8,"cm"),
legend.key.height = unit(0.5,"cm"))
p1
for (i in 1:(nrow(diff.mean) - 1))
p1 <- p1 + annotate('rect', xmin = i+0.5, xmax = i+1.5, ymin = -Inf, ymax = Inf,
fill = ifelse(i %% 2 == 0, 'white', 'gray95'))
p1
p1 <- p1 +
geom_bar(stat = "identity",position = "dodge",width = 0.7,colour = "black") +
scale_fill_manual(values=cbbPalette) + theme(legend.position = "bottom")
p1
diff.mean$var <- factor(diff.mean$var,levels = levels(abun.bar$variable))
diff.mean$p.value <- signif(diff.mean$p.value,3)
diff.mean$p.value <- as.character(diff.mean$p.value)
p2 <- ggplot(diff.mean,aes(var,estimate,fill = Group)) +
theme(panel.background = element_rect(fill = 'transparent'),
panel.grid = element_blank(),
axis.ticks.length = unit(0.4,"lines"),
axis.ticks = element_line(color='black'),
axis.line = element_line(colour = "black"),
axis.title.x=element_text(colour='black', size=12,face = "bold"),
axis.text=element_text(colour='black',size=10,face = "bold"),
axis.text.y = element_blank(),
legend.position = "none",
axis.line.y = element_blank(),
axis.ticks.y = element_blank(),
plot.title = element_text(size = 15,face = "bold",colour = "black",hjust = 0.5)) +
scale_x_discrete(limits = levels(diff.mean$var)) +
coord_flip() +
xlab("") +
ylab("Difference in mean proportions (%)") +
labs(title="95% confidence intervals")
for (i in 1:(nrow(diff.mean) - 1))
p2 <- p2 + annotate('rect', xmin = i+0.5, xmax = i+1.5, ymin = -Inf, ymax = Inf,
fill = ifelse(i %% 2 == 0, 'white', 'gray95'))
p2 <- p2 +
geom_errorbar(aes(ymin = conf.low, ymax = conf.high),
position = position_dodge(0.8), width = 0.5, size = 0.5) +
geom_point(shape = 21,size = 3) +
scale_fill_manual(values=cbbPalette) +
geom_hline(aes(yintercept = 0), linetype = 'dashed', color = 'black')
p3 <- ggplot(diff.mean,aes(var,estimate,fill = Group)) +
geom_text(aes(y = 0,x = var),label = diff.mean$p.value,
hjust = 0,fontface = "bold",inherit.aes = FALSE,size = 3) +
geom_text(aes(x = nrow(diff.mean)/2 +0.5,y = 0.85),label = "P-value (corrected)",
srt = 90,fontface = "bold",size = 5) +
coord_flip() +
ylim(c(0,1)) +
theme(panel.background = element_blank(),
panel.grid = element_blank(),
axis.line = element_blank(),
axis.ticks = element_blank(),
axis.text = element_blank(),
axis.title = element_blank())
library(patchwork)
p <- p1 + p2 + p3 + plot_layout(widths = c(4,6,2))
p
i = 1
# filename = paste(diffpath,"/",paste(allgroup[,i][1],allgroup[,i][2],sep = "_"),"stemp_P_plot.csv",sep = "")
# write.csv(diff.mean,filename)
filename = paste(diffpath,"/",paste(allgroup[,i][1],
allgroup[,i][2],sep = "_"),phyloseq::rank.names(ps)[j],"stemp_plot.pdf",sep = "")
ggsave(filename,p,width = 14,height = 6)
filename = paste(diffpath,"/",paste(allgroup[,i][1],
allgroup[,i][2],sep = "_"),phyloseq::rank.names(ps)[j],"stemp_plot.jpg",sep = "")
ggsave(filename,p,width = 14,height = 6)
detach("package:patchwork")Heatmap + Bubble diagram （热图+气泡图）
ps = readRDS("./data/dataNEW/ps_16s.rds")
heatpath = paste(otupath,"/heapmap_boplot/",sep = "")
dir.create(heatpath)
map = phyloseq::sample_data(ps)
map$ID = row.names(map)
phyloseq::sample_data(ps) = map
j = 2
ps_tem = ps %>%
ggClusterNet::scale_micro(method = "TMM") %>%
ggClusterNet::tax_glom_wt(ranks = j)
rowSD = function(x){
apply(x,1, sd)
}
rowCV = function(x){
rowSD(x)/rowMeans(x)
}
id <- ps %>%
ggClusterNet::scale_micro(method = "TMM") %>%
ggClusterNet::tax_glom_wt(ranks = j) %>%
ggClusterNet::filter_OTU_ps(100) %>%
ggClusterNet::vegan_otu() %>%
t() %>% as.data.frame() %>%rowCV %>%
sort(decreasing = TRUE) %>%
head(20) %>%
names()
ps_rela= ps_tem
heatnum = 20
label=TRUE
col_cluster=TRUE
row_cluster=TRUE
map = phyloseq::sample_data(ps_rela)
map$ID = row.names(map)
phyloseq::sample_data(ps_rela) = map
otu = as.data.frame(t(ggClusterNet::vegan_otu(ps_rela)))
otu = as.matrix(otu[id,])
ps_heatm = phyloseq::phyloseq(
phyloseq::otu_table(otu,taxa_are_rows = TRUE),
phyloseq::tax_table(ps_rela),
phyloseq::sample_data(ps_rela)
)
# print(ps_heatm)
datah <- as.data.frame(t(ggClusterNet::vegan_otu(ps_heatm)))
head(datah) tax = as.data.frame(ggClusterNet::vegan_tax(ps_heatm))
otutaxh = cbind(datah,tax)
head(otutaxh)
otutaxh$id = paste(row.names(otutaxh),otutaxh$Genus,sep = "_")
# otutaxh$id = row.names(otutaxh)
row.names(otutaxh) = otutaxh$id
data <- otutaxh[,c("id",phyloseq::sample_names(ps))]
rig <- data[,phyloseq::sample_names(ps)] %>% rowMeans() %>% as.data.frame()
head(rig) colnames(rig) = "MeanAbundance"
rig$id = row.names(rig)
rig = rig %>% dplyr::arrange(MeanAbundance)
rig$id = factor(rig$id,levels = rig$id)
p_rig = ggplot(rig) + geom_bar(aes(y = id,x = MeanAbundance),
fill = "#A54657",
stat = "identity") + theme_void()
tem = data[,phyloseq::sample_names(ps)] %>% as.matrix()
tem = scale(t(tem)) %>% t() %>%
as.data.frame()
data[,phyloseq::sample_names(ps)] = tem
# data[data > 0.3]<-0.3
mat <- data[,-1] #drop gene column as now in rows
if (col_cluster == TRUE) {
clust <- hclust(dist(mat %>% as.matrix())) # hclust with distance matrix
ggtree_plot <- ggtree::ggtree(clust)
}
if (row_cluster == TRUE) {
v_clust <- hclust(dist(mat %>% as.matrix() %>% t()))
ggtree_plot_col <- ggtree::ggtree(v_clust) + ggtree::layout_dendrogram()
}
if (label == TRUE) {
map = as.data.frame(phyloseq::sample_data(ps))
map$ID = row.names(map)
labels= ggplot(map, aes(x = ID, y=1, fill=Group)) + geom_tile() +
scale_fill_brewer(palette = 'Set1',name="Cell Type") +
theme_void()
}
map = phyloseq::sample_data(ps) %>% as.tibble() %>%
dplyr::arrange(Group) %>% as.data.frame()
map$ID
#> [1] "sample1" "sample2" "sample3" "sample4" "sample5" "sample6"
#> [7] "sample10" "sample11" "sample12" "sample7" "sample8" "sample9"
#> [13] "sample13" "sample14" "sample15" "sample16" "sample17" "sample18"
pcm = reshape2::melt(data, id = c("id"))
pcm$variable = factor(pcm$variable,levels = map$ID)
pcm$id = factor(pcm$id,levels = rig$id)
p1 = ggplot(pcm, aes(y = id, x = variable)) +
# geom_point(aes(size = value,fill = value), alpha = 0.75, shape = 21) +
geom_tile(aes(size = value,fill = value))+
scale_size_continuous(limits = c(0.000001, 100), range = c(2,25), breaks = c(0.1,0.5,1)) +
labs( y= "", x = "", size = "Relative Abundance (%)", fill = "") +
# scale_fill_manual(values = colours, guide = FALSE) +
scale_x_discrete(limits = rev(levels(pcm$variable))) +
scale_y_discrete(position = "right") +
scale_fill_gradientn(colours =colorRampPalette(RColorBrewer::brewer.pal(11,"Spectral")[11:1])(60))+
theme(
panel.background=element_blank(),
panel.grid=element_blank(),
axis.text.x = element_text(colour = "black",angle = 90)
)
colours = c( "#A54657", "#582630", "#F7EE7F", "#4DAA57","#F1A66A","#F26157", "#F9ECCC", "#679289", "#33658A",
"#F6AE2D","#86BBD8")
#----样本在y轴上
p2 = ggplot(pcm, aes(y = id, x = variable)) +
geom_point(aes(size = value,fill = value), alpha = 0.75, shape = 21) +
scale_size_continuous(limits = c(0.000001, 100), range = c(2,25), breaks = c(0.1,0.5,1)) +
labs( y= "", x = "", size = "Relative Abundance (%)", fill = "") +
# scale_fill_manual(values = colours, guide = FALSE) +
scale_x_discrete(limits = rev(levels(pcm$variable))) +
scale_y_discrete(position = "right") +
scale_fill_gradientn(colours =colorRampPalette(RColorBrewer::brewer.pal(11,"Spectral")[11:1])(60)) +
theme(
panel.background=element_blank(),
panel.grid=element_blank(),
axis.text.x = element_text(colour = "black",angle = 90)
)
p1 <- p1 %>%
aplot::insert_right(p_rig, width=.2)
p2 <- p2 %>%
aplot::insert_right(p_rig, width=.2)
if (col_cluster == TRUE) {
p1 <- p1 %>%
aplot::insert_left(ggtree_plot, width=.2)
p2 <- p2 %>%
aplot::insert_left(ggtree_plot, width=.2)
}
if (row_cluster == TRUE) {
p1 <- p1 %>%
aplot::insert_top(labels, height=.02)
p2 <- p2 %>%
aplot::insert_top(labels, height=.02)
}
if (label == TRUE) {
p1 <- p1 %>%
aplot::insert_top(ggtree_plot_col, height=.1)
p2 <- p2 %>%
aplot::insert_top(ggtree_plot_col, height=.1)
}
filename = paste(heatpath,"/",phyloseq::rank.names(ps)[j],"Topggheatmap.pdf",sep = "")
ggsave(filename,p1,width = 14,height = (6 + heatnum/10))
filename = paste(heatpath,phyloseq::rank.names(ps)[j],"Topggbubble.pdf",sep = "")
ggsave(filename,p2,width = 14,height = (6 + heatnum/10))
filename = paste(heatpath,"/",phyloseq::rank.names(ps)[j],"Topggheatmap.png",sep = "")
ggsave(filename,p1,width = 14,height = (6 + heatnum/10))
filename = paste(heatpath,phyloseq::rank.names(ps)[j],"Topggbubble.png",sep = "")
ggsave(filename,p2,width = 14,height = (6 + heatnum/10))
Multi-group difference analysis volcano plot（多组差异分析火山图）
# BiocManager::install("MetBrewer")
diffpath.1 = paste(otupath,"/Mui.Group.v/",sep = "")
dir.create(diffpath.1)
source("./function/EdgerSuper2.R")
res = EdgerSuper2 (ps = ps,group = "Group",artGroup =NULL,
j = "OTU",
path = diffpath.1
)
#> [1] "Group1" "Group2"
#> [1] "Group1" "Group3"
#> [1] "Group2" "Group3"
head(res)
res$ID = row.names(res)
datv = res
# for循环挑选每个cluster的top前5 gene symbol
tm.g <- function(data){
id = data$group %>% unique()
for (i in 1:length(id)) {
tem = filter(data,group==id[i],level != "nosig") %>%
distinct(ID,.keep_all = TRUE) %>%
top_n(5,abs(logFC))
if (i == 1) {
tem2 = tem
} else {
tem2 = rbind(tem2,tem)
}
}
return(tem2)
}
top <- tm.g(datv)
# 先画背景柱，根据数据log2FC的max值,min值来确定
#根据数据中log2FC区间确定背景柱长度：
head(datv)
tem = datv %>% group_by(group) %>% summarise(max = max(logFC),min = min(logFC)) %>% as.data.frame()
col1<-data.frame(x=tem$group,
y=tem$max)
col2<-data.frame(x=tem$group,
y=tem$min)
# 绘制背景柱
p1 <- ggplot()+
geom_col(data = col1,
mapping = aes(x = x,y = y),
fill = "#dcdcdc",alpha = 0.6)+
geom_col(data = col2,
mapping = aes(x = x,y = y),
fill = "#dcdcdc",alpha = 0.6)
p1
#把散点火山图叠加到背景柱上：
head(datv)
p2 <- ggplot()+
geom_col(data = col1,
mapping = aes(x = x,y = y),
fill = "#dcdcdc",alpha = 0.6)+
geom_col(data = col2,
mapping = aes(x = x,y = y),
fill = "#dcdcdc",alpha = 0.6)+
geom_jitter(data = datv,
aes(x =group , y = logFC, color =level ),
size = 1,
width =0.4)+
scale_color_manual(name=NULL,
values = c("#4393C3","#FC4E2A","grey40"))+
labs(x="",y="log2(FoldChange)")
p2
# 添加X轴的分组色块标签：
dfcol<-data.frame(x=tem$group,
y=0,
label=tem$group)
# 添加分组色块标签
dfcol$group <- tem$group
# 加载包
library(RColorBrewer)
library(MetBrewer)
# BiocManager::install("MetBrewer")
# 自定义分组色块的颜色
tile_color <- met.brewer("Thomas",length(tem$group))
# 在图中镶嵌色块
p3 <- p2 + geom_tile(data = dfcol,
aes(x=x,y=y),
height=1.75,
color = "black",
fill = tile_color,
alpha = 0.6,
show.legend = F)+
geom_text(data=dfcol,
aes(x=x,y=y,label=group),
size =3.5,
color ="white") + theme_classic()
p3
library(ggrepel)
p4<-p3+geom_text_repel(
data=top,
aes(x=group,y=logFC,label=ID),
force = 1.2,
arrow = arrow(length = unit(0.008, "npc"),
type = "open", ends = "last"))
p4
# 去除背景，美化图片
p5 <- p4+
theme_minimal()+
theme(
axis.title = element_text(size = 18,
color = "black",
face = "bold"),
axis.line.y = element_line(color = "black",
size = 1.2),
axis.line.x = element_blank(),
axis.text.x = element_blank(),
panel.grid = element_blank(),
legend.position = "top",
legend.direction = "vertical",
legend.justification = c(1,0),
legend.text = element_text(size = 12)
)
p5
# return(list(p5,p3,datv,top))
p = p3
p
filename = paste(diffpath.1,"/","Mui.group.volcano.pdf",sep = "")
ggsave(filename,p,width = 12,height = 6,limitsize = FALSE)3.Biomarker identification （3.生物标志物鉴别）
LEfSe analysis（LEfSe分析）
lefsepath = paste(otupath,"/lefse_R_plot/",sep = "")
dir.create(lefsepath)
ps = readRDS("./data/dataNEW/ps_16s.rds")
library(ggtree)
p_base = function(ps,Top = 100,ranks = 6) {
alltax = ps %>%
ggClusterNet::tax_glom_wt(ranks = ranks ) %>%
ggClusterNet::filter_OTU_ps(Top) %>%
ggClusterNet::vegan_tax() %>%
as.data.frame()
alltax$OTU = row.names(alltax)
alltax$Kingdom = paste(alltax$Kingdom,sep = "_Rank_")
for (i in 2:ranks) {
alltax[,i]= paste(alltax[,i - 1],alltax[,i],sep = "_Rank_")
}
alltax[is.na(alltax)] = "Unknown"
trda <- MicrobiotaProcess::convert_to_treedata(alltax)
p <- ggtree(trda, layout="circular", size=0.2, xlim=c(30,NA)) +
geom_point(
pch = 21,
size=3,
alpha=1,
fill = "#FFFFB3"
)
p$data$lab2 <- p$data$label %>% strsplit( "_Rank_") %>%
sapply(
function(x) x[length(x)]
)
p$data$lab2 = gsub("st__","",p$data$lab2 )
p$data$nodeSize = 1
return(p)
}
LDA_Micro = function(ps = ps,
Top = 100,
ranks = 6,
p.lvl = 0.05,
lda.lvl = 2,
seed = 11,
adjust.p = F
){
alltax = ps %>%
ggClusterNet::tax_glom_wt(ranks = ranks ) %>%
phyloseq::filter_taxa(function(x) sum(x ) > 0 , TRUE) %>%
ggClusterNet::filter_OTU_ps(Top) %>%
ggClusterNet::vegan_tax() %>%
as.data.frame()
alltax$OTU = row.names(alltax)
alltax$Kingdom = paste(alltax$Kingdom,sep = "_Rank_")
for (i in 2:ranks) {
alltax[,i]= paste(alltax[,i - 1],alltax[,i],sep = "_Rank_")
}
otu = ps %>%
ggClusterNet::tax_glom_wt(ranks = ranks ) %>%
phyloseq::filter_taxa(function(x) sum(x ) > 0 , TRUE) %>%
ggClusterNet::filter_OTU_ps(Top) %>%
ggClusterNet::vegan_otu() %>%
t() %>%
as.data.frame()
otu_tax = merge(otu,alltax,by = "row.names",all = F)
head(otu_tax)
tem = colnames(alltax)[-length(colnames(alltax))]
i = 1
tem2 = c("k__","p__","c__","o__","f__","g__","s__","st__")
for (i in 1:ranks) {
rank1 <- otu_tax %>%
dplyr::group_by(!!sym(tem[i])) %>%
dplyr::summarise_if(is.numeric, sum, na.rm = TRUE)
colnames(rank1)[1] = "id"
rank1$id = paste(tem2[i],rank1$id,sep = "")
if (i == 1) {
all = rank1
}
if (i != 1) {
all = rbind(all,rank1)
}
}
#--LDA排序#--------
data1 = as.data.frame(all)
row.names(data1) = data1$id
data1$id = NULL
#-构建phylose对象
ps_G_graphlan = phyloseq::phyloseq(phyloseq::otu_table(as.matrix(data1),taxa_are_rows = TRUE),
phyloseq::sample_data(ps))# %>% filter_taxa(function(x) sum(x ) > 0 , TRUE)
ps_G_graphlan
#----提取OTU表格
otu = as.data.frame((ggClusterNet::vegan_otu(ps_G_graphlan)))
map = as.data.frame(phyloseq::sample_data(ps_G_graphlan))
# otu = (otu_table)
claslbl= map$Group %>% as.factor()
set.seed(seed)
#KW rank sum test
rawpvalues <- apply(otu, 2, function(x) kruskal.test(x, claslbl)$p.value);
#--得到计算后得到的p值
ord.inx <- order(rawpvalues)
rawpvalues <- rawpvalues[ord.inx]
clapvalues <- p.adjust(rawpvalues, method ="fdr")
# p.adjust
wil_datadf <- as.data.frame(otu[,ord.inx])
ldares <- MASS::lda(claslbl ~ .,data = wil_datadf)
# ldares
ldamean <- as.data.frame(t(ldares$means))
ldamean
class_no <<- length(unique(claslbl))
ldamean$max <- apply(ldamean[,1:class_no],1,max);
ldamean$min <- apply(ldamean[,1:class_no],1,min);
#---计算LDA
ldamean$LDAscore <- signif(log10(1+abs(ldamean$max-ldamean$min)/2),digits=3);
head(ldamean)
a = rep("A",length(ldamean$max))
for (i in 1:length(ldamean$max)) {
name =colnames(ldamean[,1:class_no])
a[i] = name[ldamean[,1:class_no][i,] %in% ldamean$max[i]]
}
ldamean$class = a
tem1 = row.names(ldamean)
tem1 %>% as.character()
ldamean$Pvalues <- signif(rawpvalues[match(row.names(ldamean),names(rawpvalues))],digits=5)
ldamean$FDR <- signif(clapvalues,digits=5)
resTable <- ldamean
rawNms <- rownames(resTable);
rownames(resTable) <- gsub("`", '', rawNms);
if (adjust.p) {
de.Num <- sum(clapvalues <= p.lvl & ldamean$LDAscore>=lda.lvl)
} else {
de.Num <- sum(rawpvalues <= p.lvl & ldamean$LDAscore>=lda.lvl)
}
if(de.Num == 0){
current.msg <<- "No significant features were identified with given criteria.";
}else{
current.msg <<- paste("A total of", de.Num, "significant features with given criteria.")
}
print(current.msg)
# sort by p value
ord.inx <- order(resTable$Pvalues, resTable$LDAscore)
resTable <- resTable[ord.inx, ,drop=FALSE]
resTable <- resTable[,c(ncol(resTable),1:(ncol(resTable)-1))]
resTable <- resTable[,c(ncol(resTable),1:(ncol(resTable)-1))]
ldamean$Pvalues[is.na(ldamean$Pvalues)] = 1
# resTable %>% tail()
if (adjust.p) {
taxtree = resTable[clapvalues <=p.lvl & ldamean$LDAscore>=lda.lvl,]
} else {
# taxtree = resTable[ldamean$Pvalues <=p.lvl & ldamean$LDAscore>=lda.lvl,]
taxtree = resTable[ldamean$Pvalues <=p.lvl,]
}
#-提取所需要的颜色
colour = c('darkgreen','red',"blue","#4DAF4A", "#984EA3", "#FF7F00", "#FFFF33", "#A65628", "#F781BF")
selececol = colour[1:length(levels(as.factor(taxtree$class)))]
names(selececol) = levels(as.factor(taxtree$class))
A = rep("a",length(row.names(taxtree)))
for (i in 1:length(row.names(taxtree))) {
A[i] = selececol [taxtree$class[i]]
}
taxtree$color = A
# taxtree <- taxtree[row.names(taxtree) != "k__Bacteria",]
# node_ids <- p0$data
# anno <- rep("white", nrow(p1$data))
lefse_lists = data.frame(node=row.names(taxtree),
color=A,
Group = taxtree$class,
stringsAsFactors = FALSE
)
return(list(lefse_lists,taxtree))
}
# gtree = p1
# anno.data= tablda[[1]]
# alpha=0.3
# anno.depth = 2
library(patchwork)
clade.anno_wt <- function(gtree, anno.data, alpha = 0.2, anno.depth = 5, anno.x = 10,
anno.y = 40){
short.labs <- c(letters,paste(letters,1:500,sep = ""))
get_offset <- function(x) {
(x * 0.2 + 0.2)^2
}
get_angle <- function(node) {
data <- gtree$data
sp <- tidytree::offspring(data, node)$node
sp2 <- c(sp, node)
sp.df <- data[match(sp2, data$node), ]
mean(range(sp.df$angle))
}
anno.data <- dplyr::arrange(anno.data, node)
hilight.color <- anno.data$color
node_list <- anno.data$node
node_ids <- (gtree$data %>% filter(label %in% node_list) %>%
arrange(label))$node
anno <- rep("yellow", nrow(gtree$data))
#---添加阴影#-------
i = 1
for (i in 1:length(node_ids)) {
n <- node_ids[i]
color <- hilight.color[i]
anno[n] <- color
mapping <- gtree$data %>% filter(node == n)
nodeClass <- as.numeric(mapping$nodeDepth)
offset <- get_offset(nodeClass)
gtree <- gtree + geom_hilight(node = n, fill = color,
alpha = alpha, extend = offset)
}
# gtree$layers <- rev(gtree$layers)
# gtree <- gtree + geom_point2(aes(size = I(nodeSize)), fill = anno,
# shape = 21)
short.labs.anno <- NULL
i = 1
# gtree$data
#--添加标签#--------
for (i in 1:length(node_ids)) {
n <- node_ids[i]
mapping <- gtree$data %>% filter(node == n)
nodeClass <- as.numeric(mapping$nodeDepth)
if (nodeClass <= anno.depth) {
lab <- short.labs[1]
short.labs <- short.labs[-1]
if (is.null(short.labs.anno)) {
short.labs.anno = data.frame(lab = lab, annot = mapping$lab2,
stringsAsFactors = F)
}else {
short.labs.anno = rbind(short.labs.anno, c(lab,mapping$lab2))
}
} else {
lab <- mapping$lab2
}
offset <- get_offset(nodeClass) - 0.4
angle <- get_angle(n) + 90
gtree <- gtree + geom_cladelabel(node = n, label = lab,
angle = angle, fontsize = 1 + sqrt(nodeClass),
offset = offset, barsize = NA, hjust = 0.5)
}
if (!is.null(short.labs.anno)) {
anno_shapes = sapply(short.labs.anno$lab, utf8ToInt)
stable.p <- ggpubr::ggtexttable(short.labs.anno, rows = NULL,
theme = ggpubr::ttheme(
colnames.style = ggpubr::colnames_style(fill = "white"),
tbody.style = ggpubr::tbody_style(fill = ggpubr::get_palette("RdBu", 6))
))
}
y = (1:length(unique(anno.data$Group)))
pleg <- ggplot() + geom_point2(aes(
y = y,
x = rep(1,length(unique(anno.data$color))),fill = as.factor(1:length(unique(anno.data$Group)))
),pch = 21,size = 2) +
geom_text(aes( y = y,
x = rep(1,length(unique(anno.data$color))),label = unique(anno.data$Group) ),
hjust = -1
) + scale_fill_manual(values = unique(anno.data$color),guide = F) +
theme_void()
layout <- "
AAAAAABB#
AAAAAABB#
AAAAAABBC
AAAAAABBC
AAAAAABB#
"
if (is.null(short.labs.anno)) {
gtree <- gtree + pleg + plot_layout(design = layout)
} else {
gtree <- gtree + stable.p+ pleg + plot_layout(design = layout)
}
}
lefse_bar = function(taxtree = tablda[[2]]){
taxtree = tablda[[2]]
taxtree$ID = row.names(taxtree)
head(taxtree)
taxtree$ID = gsub("_Rank_",";",taxtree$ID)
taxtree <- taxtree %>%
arrange(class,LDAscore)
taxtree$ID = factor(taxtree$ID,levels=taxtree$ID)
taxtree$class = factor(taxtree$class,levels = unique(taxtree$class))
pbar <- ggplot(taxtree) + geom_bar(aes(y =ID, x = LDAscore,fill = class),stat = "identity") +
scale_fill_manual(values = unique(taxtree$color)) + mytheme1 +
scale_x_continuous(limits = c(0,max(taxtree$LDAscore)*1.2))
}
for (j in 2:4) {
p1 <- p_base(ps,Top = 200,ranks =j)
p1
tablda = LDA_Micro(ps = ps,
Top = 200,
ranks = j,
p.lvl = 0.05,
lda.lvl = 2,
seed = 11,
adjust.p = F)
p2 <- clade.anno_wt(p1, tablda[[1]], alpha=0.3,anno.depth = 2)
p2
FileName <- paste(lefsepath,j,"_tree_lefse", ".pdf", sep = "")
ggsave(FileName,p2,width = 15,height = 10)
FileName <- paste(lefsepath,j,"_tree_lefse", ".png", sep = "")
ggsave(FileName,p2,width = 15,height = 10)
p <- lefse_bar(taxtree = tablda[[2]])
FileName <- paste(lefsepath,j,"_bar_lefse", ".pdf", sep = "")
ggsave(FileName, p, width = 15, height =9)
FileName <- paste(lefsepath,j,"_bar_lefse", ".png", sep = "")
ggsave(FileName, p, width = 15, height =9)
res = tablda[[2]]
FileName <- paste(lefsepath,j,"_tree_lefse_data", ".csv", sep = "")
write.csv(res,FileName,quote = F)
}
#> [1] "A total of 5 significant features with given criteria."
#> [1] "A total of 14 significant features with given criteria."
#> [1] "A total of 22 significant features with given criteria."Machine learning（机器学习）
matpath = paste(otupath,"/Machine_learing/",sep = "")
dir.create(matpath )
MicroRF <- function(otu = NULL,tax = NULL,map = NULL,tree = NULL,
ps = NULL,group = "Group",optimal = 20,rfcv = FALSE,nrfcvnum = 5,min = -1,max = 5
){
ps = ggClusterNet::inputMicro(otu,tax,map,tree,ps,group = group) %>% ggClusterNet::scale_micro()
map = as.data.frame(phyloseq::sample_data(ps))
#-scaleing relative abundancce#----
mapping = as.data.frame(phyloseq::sample_data(ps))
otutab = as.data.frame((ggClusterNet::vegan_otu(ps)))
colnames(otutab) = paste("wentao",colnames(otutab),sep = "")
# Set classification info.
otutab$group = factor(mapping$Group)
# colnames(otutab) <- gsub("-","_",colnames(otutab))
model_rf= randomForest::randomForest(group ~ ., data=otutab, importance=TRUE, proximity=TRUE)
print(model_rf)
#--提取混淆矩阵
Confusion_matrix <- as.data.frame(model_rf$confusion)
Confusion_matrix$class.error <- round(Confusion_matrix$class.error,3)
Confusion_matrix$Group = row.names(Confusion_matrix)
Confusion_matrix <- dplyr::select(Confusion_matrix , Group, everything())
#-提取正确率
model_Accuracy_rates <- paste(round(100-tail(model_rf$err.rate[,1],1)*100,2),"%",sep = "")
model_Accuracy_rates = data.frame(ID = "model Accuracy rates",model_Accuracy_rates = model_Accuracy_rates)
colnames(model_Accuracy_rates) = c("Random foreest","Fu wilt model")
tab2 <- ggpubr::ggtexttable(Confusion_matrix, rows = NULL)
tab1 <- ggpubr::ggtexttable(model_Accuracy_rates, rows = NULL)
library(patchwork)
pn <- tab1/tab2
if (rfcv) {
result = Micro.rfcv(otu = NULL,tax = NULL,map = NULL,tree = NULL ,ps = ps_rela,group = "Group",optimal = 20,nrfcvnum = 6)
prfcv = result[[1]]# plot rfcv
# result[[2]]# plotdata
rfcvtable = result[[3]]# table rfcv
} else{
prfcv = NULL
rfcvtable = NULL
}
#------------k可视化
###### inportant OTU picked out and plot
a=as.data.frame(round(randomForest::importance(model_rf), 2))
a$id=row.names(a)
row.names(a) = gsub("wentao","",row.names(a))
a$id = gsub("wentao","",a$id)
a2<- dplyr::arrange(a, desc(MeanDecreaseAccuracy)) %>% as.data.frame()
row.names(a2)=a2$id
a3=head(a2,n=optimal)
taxonomy <- as.data.frame( ggClusterNet::vegan_tax(ps))
tax = taxonomy[rownames(a3),]
a3 = merge(a3,tax,by = "row.names",all = F)
row.names(a3) = a3$Row.names
a3$Row.names = NULL
OTU = ggClusterNet::vegan_otu(ps)
### pice mapping
design = as.data.frame(phyloseq::sample_data(ps))
#mean abundance by groups
iris.split <- split(as.data.frame(OTU),as.factor(design$Group))
iris.apply <- lapply(iris.split,function(x)colMeans(x,na.rm = TRUE))
norm2 <- do.call(rbind,iris.apply)%>% # combine result
t()
colnames(norm2) = paste(colnames(norm2),"mean",sep = "")
ind_fal = merge(a3,norm2,by = "row.names",all = F)
head(ind_fal)
# plot fire bar 1
p1 <- ggplot(a3, aes(x = MeanDecreaseAccuracy, y = reorder(id,MeanDecreaseAccuracy))) +
geom_point(size=6,pch=21,fill = "#9ACD32",color = "#9ACD32")+
geom_segment(aes(yend=id),xend=0,size=3,color = "#9ACD32")+
geom_label(aes(x =MeanDecreaseAccuracy*1.1, label = Phylum),size = 3)
# plot 2
a3<- dplyr::arrange(a3, desc(MeanDecreaseAccuracy))
a3$iid = paste(1:length(a3$id))
angle1 = 90 - 360 * ( as.numeric(a3$iid) - 0.5) /length(a3$id)
a3$id = factor(a3$id,levels = a3$id)
p2 = a3 %>%
ggplot(aes(x = factor(id), y = MeanDecreaseAccuracy ,label = Phylum)) +
geom_bar(stat = 'identity', position = 'dodge',fill = "blue") +
# scale_fill_manual(values = mi)+
geom_text(hjust = 0, angle = angle1, alpha = 1) +
coord_polar() +
ggtitle('') +
ylim(c(min,max))+
theme_void()
p2
return(list(p1,p2,prfcv,rfcvtable,a3,pn))
}
Micro.rfcv = function(otu = NULL,tax = NULL,map = NULL,tree = NULL,
ps = NULL,group = "Group",optimal = 20,nrfcvnum = 5){
ps = ggClusterNet::inputMicro(otu,tax,map,tree,ps,group = group)
otutab = as.data.frame((ggClusterNet::vegan_otu(ps)))
# Set classification info.
otutab$group = factor(mapping$Group)
colnames(otutab) <- gsub("-","_",colnames(otutab))
# rfcv for select···
n = ncol(otutab)-1
myotutab_t= otutab[1:n]
set.seed(315)
result= rfcv(myotutab_t, otutab$group, cv.fold=5, scale = "log", step = 0.9)
# with(result, plot(n.var, error.cv, log="x", type="o", lwd=2))
result1 = result
error.cv = data.frame(num = result$n.var, error.1 = result$error.cv)
for (i in 316:(314+ nrfcvnum)){
print(i)
set.seed(i)
result= rfcv(myotutab_t, otutab$group, cv.fold=5, scale = "log", step = 0.9)
error.cv = cbind(error.cv, result$error.cv)
}
n.var = error.cv$num
error.cv = error.cv[,2:6]
colnames(error.cv) = paste('err',1:5,sep='.')
err.mean = apply(error.cv,1,mean)
allerr = data.frame(num=n.var,err.mean=err.mean,error.cv)
head(allerr)
data <- gather(allerr, key = "group", value = "value",-num)
head(data)
p <- ggplot() +
geom_line(data = data,aes(x = num, y = value,group = group), colour = 'grey') +
geom_line(aes(x = allerr$num, y = allerr$err.mean), colour = 'black') +
coord_trans(x = "log2") +
scale_x_continuous(breaks = c(1, 2, 5, 10, 20, 30, 50, 100, 200)) + # , max(allerr$num)
labs(title=paste('Training set (n = ', dim(otutab)[1],')', sep = ''),
x='Number of families ', y='Cross-validation error rate') +
annotate("text", x = optimal, y = max(allerr$err.mean), label=paste("optimal = ", optimal, sep=""))
return(list(plot = p,plotdata = data,origdata = allerr))
}
MicroRoc <- function(otu = NULL,tax = NULL,map = NULL,tree = NULL,
ps = NULL,group = "Group",repnum = 5){
ps = ggClusterNet::inputMicro(otu,tax,map,tree,ps,group = group)
mapping = as.data.frame(phyloseq::sample_data(ps))
otutab = as.data.frame(t(ggClusterNet::vegan_otu(ps)))
colnames(otutab) <- gsub("-","_",colnames(otutab))
test = as.data.frame(t(otutab))
test$group = factor(mapping$Group)
colnames(test) = paste("OTU",colnames(test),sep = "")
# random forest
test = dplyr::select(test,OTUgroup,everything())
train = test
folds <- createFolds(y=test[,1],k=repnum)
AUC =c()
max=0
num=0
fc<-as.numeric()#fc 为测试数据真实分组信息
mod_pre<-as.numeric()
for(i in 1:repnum){
fold_test<-train[folds[[i]],]
fold_train<-train[-folds[[i]],]
colnames(fold_test) <- gsub("-","_",colnames(fold_test))
colnames(fold_train) <- gsub("-","_",colnames(fold_train))
model<-randomForest(OTUgroup~.,data=fold_train, importance=TRUE, proximity=TRUE)
model_pre<-predict(model,newdata = fold_test,type="prob")
fc<-append(fc,as.factor(fold_test$OTUgroup))
mod_pre<-append(mod_pre,model_pre[,2])
}
#- pick data and plot
pred <- prediction(mod_pre, fc)
perf <- performance(pred,"tpr","fpr")
x <- unlist(perf@x.values) ##提取x值
y <- unlist(perf@y.values)
plotdata <- data.frame(x,y)
names(plotdata) <- c("x", "y")
AUC[1] = paste("rf AUC:",round(performance(pred,'auc')@y.values[[1]],3),sep = " ")
head(plotdata)
g0 <- ggplot(plotdata) +
geom_path(aes(x = x, y = y, colour = x), size=1,color = "red") +
labs(x = "False positive rate", y = "Ture positive rate") + # , title ="Random Forest"
annotate("text", x=0.75, y=0.5, label=paste("Red: ",AUC[1],sep = ""))
df<-cbind(fc,as.numeric(mod_pre))
#-svm
max=0
num=0
fc<-as.numeric()
mod_pre<-as.numeric()
for(i in 1:repnum){
fold_test<-train[folds[[i]],]
# head(fold_test)
fold_train<-train[-folds[[i]],]
model<-svm(OTUgroup~.,data=fold_train,probability=TRUE)
model
model_pre<-predict(model,newdata = fold_test,decision.values = TRUE, probability = TRUE)
fc<-append(fc,as.numeric(fold_test$OTUgroup))
mod_pre<-append(mod_pre,as.numeric(attr(model_pre, "probabilities")[,2]))
}
pred <- prediction(mod_pre, fc)
perf <- performance(pred,"tpr","fpr")
x <- unlist(perf@x.values)
y <- unlist(perf@y.values)
plotdata <- data.frame(x,y)
names(plotdata) <- c("x", "y")
AUC[2] = paste("svm AUC:",round(performance(pred,'auc')@y.values[[1]],3),sep = " ")
g1 <- g0 +
geom_path(data = plotdata,aes(x = x, y = y, colour = x), size=1,color = "blue") +
# labs(x = "False positive rate", y = "Ture positive rate") +
annotate("text", x=0.75, y=0.4, label=paste("Blue: ",AUC[2],sep = ""))
df<-cbind(df,cbind(fc,mod_pre))
#GLM
max=0
num=0
fc<-as.numeric()
mod_pre<-as.numeric()
for(i in 1:repnum){
fold_test<-train[folds[[i]],]
fold_train<-train[-folds[[i]],]
model<-glm(OTUgroup~.,family='binomial',data=fold_train)
model
model_pre<-predict(model,type='response',newdata=fold_test)
model_pre
fc<-append(fc,fold_test$OTUgroup)
mod_pre<-append(mod_pre,as.numeric(model_pre))
}
pred <- prediction(mod_pre, fc)
perf <- performance(pred,"tpr","fpr")
x <- unlist(perf@x.values) ##提取x值
y <- unlist(perf@y.values)
plotdata <- data.frame(x,y)
names(plotdata) <- c("x", "y")
AUC[3] = paste("GLM AUC:",round(performance(pred,'auc')@y.values[[1]],3),sep = " ")
g2 <- g1 +
geom_path(data = plotdata,aes(x = x, y = y, colour = x), size=1,color = "black") +
labs(x = "False positive rate", y = "Ture positive rate") +
annotate("text", x=0.75, y=0.3, label=paste("Black: ",AUC[3],sep = ""))
g2
df<-cbind(df,cbind(fc,mod_pre))
return(list(g2,AUC,df))
}
MicroRF_GC <- function(otu = NULL,tax = NULL,map = NULL,tree = NULL,
ps = NULL,group = "Group",optimal = 20,rfcv = FALSE,nrfcvnum = 5,min = -1,max = 5
){
ps = ggClusterNet::inputMicro(otu,tax,map,tree,ps,group = group) %>% ggClusterNet::scale_micro()
map = as.data.frame(phyloseq::sample_data(ps))
#-scaleing relative abundancce#----
mapping = as.data.frame(phyloseq::sample_data(ps))
otutab = as.data.frame((ggClusterNet::vegan_otu(ps)))
tem = colnames(otutab)
colnames(otutab) = paste("RE",1:length(colnames(otutab)),sep = "")
tem2 = data.frame(ID = colnames(otutab),name = tem)
# colnames(otutab) = paste("wentao",colnames(otutab),sep = "")
# Set classification info.
otutab$group = factor(mapping$Group)
# colnames(otutab) <- gsub("-","_",colnames(otutab))
model_rf= randomForest::randomForest(group ~ ., data=otutab, importance=TRUE, proximity=TRUE)
print(model_rf)
#--提取混淆矩阵
Confusion_matrix <- as.data.frame(model_rf$confusion)
Confusion_matrix$class.error <- round(Confusion_matrix$class.error,3)
Confusion_matrix$Group = row.names(Confusion_matrix)
Confusion_matrix <- dplyr::select(Confusion_matrix , Group, everything())
#-提取正确率
model_Accuracy_rates <- paste(round(100-tail(model_rf$err.rate[,1],1)*100,2),"%",sep = "")
model_Accuracy_rates = data.frame(ID = "model Accuracy rates",model_Accuracy_rates = model_Accuracy_rates)
colnames(model_Accuracy_rates) = c("Random foreest","Fu wilt model")
tab2 <- ggpubr::ggtexttable(Confusion_matrix, rows = NULL)
tab1 <- ggpubr::ggtexttable(model_Accuracy_rates, rows = NULL)
library(patchwork)
pn <- tab1/tab2
if (rfcv) {
result = Micro.rfcv(otu = NULL,tax = NULL,map = NULL,tree = NULL ,ps = ps_rela,group = "Group",optimal = 20,nrfcvnum = 6)
prfcv = result[[1]]# plot rfcv
# result[[2]]# plotdata
rfcvtable = result[[3]]# table rfcv
} else{
prfcv = NULL
rfcvtable = NULL
}
#------------k可视化
###### inportant OTU picked out and plot
a=as.data.frame(round(randomForest::importance(model_rf), 2))
a$id=row.names(a)
head(a)
row.names(a) = tem
a$id = tem
a2<- dplyr::arrange(a, desc(MeanDecreaseAccuracy)) %>% as.data.frame()
row.names(a2)=a2$id
# optimal = 40
a3=head(a2,n=optimal)
OTU = ggClusterNet::vegan_otu(ps)
### pice mapping
design = as.data.frame(phyloseq::sample_data(ps))
#mean abundance by groups
iris.split <- split(as.data.frame(OTU),as.factor(design$Group))
iris.apply <- lapply(iris.split,function(x)colMeans(x,na.rm = TRUE))
norm2 <- do.call(rbind,iris.apply)%>% # combine result
t()
colnames(norm2) = paste(colnames(norm2),"mean",sep = "")
ind_fal = merge(a3,norm2,by = "row.names",all = F)
head(ind_fal)
# plot fire bar 1
head(a3)
p1 <- ggplot(a3, aes(x = MeanDecreaseAccuracy, y = reorder(id,MeanDecreaseAccuracy))) +
geom_point(size=6,pch=21,fill = "#9ACD32",color = "#9ACD32")+
geom_segment(aes(yend=id),xend=0,size=3,color = "#9ACD32")+
geom_label(aes(x =MeanDecreaseAccuracy*1.1, label = id),size = 3) + theme_classic()
# plot 2
a3<- dplyr::arrange(a3, desc(MeanDecreaseAccuracy))
a3$iid = paste(1:length(a3$id))
angle1 = 90 - 360 * ( as.numeric(a3$iid) - 0.5) /length(a3$id)
a3$id = factor(a3$id,levels = a3$id)
p2 = a3 %>%
ggplot(aes(x = factor(id), y = MeanDecreaseAccuracy ,label = id)) +
geom_bar(stat = 'identity', position = 'dodge',fill = "blue") +
# scale_fill_manual(values = mi)+
geom_text(hjust = 0, angle = angle1, alpha = 1) +
coord_polar() +
# ylim(c(min,max))+
theme_void()
p2
return(list(p1,p2,prfcv,rfcvtable,a3,pn))
}
library(randomForest)
library(caret)
library(ROCR) ##用于计算ROC
library(e1071)
ps = ps %>% subset_samples.wt("Group","Group1",TRUE) %>% filter_OTU_ps(300)
#--三种机器学习方法评测
result = MicroRoc( ps = ps,group = "Group")
#--提取roc曲线
p <- result[[1]] +
mytheme1
p
#提取AUC值
data <- result[[2]]
filename = paste(matpath,"/three_method_AUCvalue.csv",sep = "")
write.csv(data,filename,quote = F)
data <- result[[3]]
filename = paste(matpath,"/three_method_AUCdata.csv",sep = "")
write.csv(data,filename,quote = F)
filename = paste(matpath,"/three_method_AUC_plot.pdf",sep = "")
ggsave(filename,p,width = 8,height = 8)
filename = paste(matpath,"/three_method_AUC_plot.jpg",sep = "")
ggsave(filename,p,width = 8,height = 8)
mapping = as.data.frame(phyloseq::sample_data(ps))
ps_rela = ps
#--随机森林全套-如果圈图尚未显示前面几个，就设定max大一点
result = MicroRF(ps = ps,
group = "Group",
optimal = 40,rfcv = TRUE,nrfcvnum = 5,
min = -1,max = 5)
#>
#> Call:
#> randomForest(formula = group ~ ., data = otutab, importance = TRUE, proximity = TRUE)
#> Type of random forest: classification
#> Number of trees: 500
#> No. of variables tried at each split: 17
#>
#> OOB estimate of error rate: 0%
#> Confusion matrix:
#> Group2 Group3 class.error
#> Group2 6 0 0
#> Group3 0 6 0
#> [1] 316
#> [1] 317
#> [1] 318
#> [1] 319
#> [1] 320
#火柴图展示前二十个重要的OTU
p <- result[[1]] +
mytheme1
p
optimal = 40
filename = paste(matpath,"/randonforest_loading.pdf",sep = "")
ggsave(filename,p,width = 8,height = optimal/2)
filename = paste(matpath,"/randonforest_loading.jpg",sep = "")
ggsave(filename,p,width = 8,height = optimal/2)
# 圈图展示
p <- result[[2]]
p
filename = paste(matpath,"/randonforest_loading_circle.pdf",sep = "")
ggsave(filename,p,width = 8,height = 10)
filename = paste(matpath,"/randonforest_loading_circle.jpg",sep = "")
ggsave(filename,p,width = 8,height = 10)
p <- result[[6]]
p
filename = paste(matpath,"/Show_model.pdf",sep = "")
ggsave(filename,p,width = 8,height = 4)
filename = paste(matpath,"/Show_model.jpg",sep = "")
ggsave(filename,p,width = 8,height = 4)
# 展示交叉验证结果
p <- result[[3]]
filename = paste(matpath,"/randonforest_cross_check.pdf",sep = "")
ggsave(filename,p,width = 8,height = 12)
data <- result[[4]]
filename = paste(matpath,"/randomforest_cross_data.csv",sep = "")
write.csv(data,filename,quote = F)
data <- result[[5]]
filename = paste(matpath,"/randomforest_data.csv",sep = "")
write.csv(data,filename,quote = F)4.Correlation network analysis（4.相关和网络分析）
network analysis-1（网络分析1）
ps = readRDS("./data/dataNEW/ps_16s.rds")
netpath = paste(otupath,"/network2/",sep = "")
dir.create(netpath)
library(igraph)
library(sna)
library(phyloseq)
library(ggClusterNet)
result = ggClusterNet::network.2(ps = ps,
N = 500,
big = TRUE,
maxnode = 5,
select_layout = TRUE,
layout_net = "model_maptree2",
r.threshold=0.6,
p.threshold=0.05,
label = FALSE,
path = netpath,
zipi = F,
ncol = gnum,
nrow = 1,
# method = "sparcc",
fill = "Phylum"
)
#> [1] "Group1"
#> [1] "cor matrix culculating over"
#> [1] "Group2"
#> [1] "cor matrix culculating over"
#> [1] "Group3"
#> [1] "cor matrix culculating over"
# 全部样本的网络比对
p4_1 = result[[1]] + mytheme1
# 全部样本网络参数比对
data = result[[2]]
plotname1 = paste(netpath,"/network_all.pdf",sep = "")
ggsave(plotname1, p4_1,width = 6*gnum,height = 6,limitsize = FALSE)
# plotname1 = paste(netpath,"/network_all.jpg",sep = "")
# ggsave(plotname1, p4_1,width = 16*gnum,height = 16)
tablename <- paste(netpath,"/co-occurrence_Grobel_net",".csv",sep = "")
write.csv(data,tablename)
# 全部样本的网络比对
p4_2 = result[[3]] +
scale_fill_brewer(palette = "Paired") +
mytheme1
plotname1 = paste(netpath,"/network_all_cover.pdf",sep = "")
ggsave(plotname1, p4_2,width = 10*gnum,height = 10,limitsize = FALSE)network analysis-2（网络分析2）
netpath = paste(otupath,"/network_igraph/",sep = "")
dir.create(netpath)
# map = sample_data(ps)
# map$Group = "one"
# sample_data(ps16s) = map
library(igraph)
library(ggnewscale)
result = network.i(ps = ps,
N = 500,
r.threshold=0.8,
big = TRUE,
select_layout = TRUE,
method = "pearson",
scale = FALSE,
layout_net = "model_igraph2",
p.threshold=0.05,
label = FALSE,
path = netpath ,
ncol = gnum,
nrow = 1,
zipi = FALSE,
order = NULL
)
#> [1] "Group1"
#> [1] "cor matrix culculating over"
#> [1] "Group2"
#> [1] "cor matrix culculating over"
#> [1] "Group3"
#> [1] "cor matrix culculating over"
p1 = result[[1]]
dat = result[[2]]
tablename <- paste(netpath,"/co-occurrence_Grobel_net",".csv",sep = "")
write.csv(dat,tablename)
p = result[[5]]
plotname1 = paste(netpath,"/network_all.pdf",sep = "")
ggsave(plotname1, p1,width = 3*gnum,height = 2.5,limitsize = FALSE)
plotname1 = paste(netpath,"/network_all2.pdf",sep = "")
ggsave(plotname1, p,width = 6*gnum,height = 6,limitsize = FALSE)network stability analysis（网络稳定性分析）
library(ggClusterNet)
library(phyloseq)
library(tidyverse)
library(igraph)
library(tidyfst)
netpath = paste(otupath,"/network_stab/",sep = "")
dir.create(netpath)
res = module.compare.m(
ps = ps,
Top = 200,
degree = TRUE,
zipi = FALSE,
r.threshold= 0.8,
p.threshold=0.05,
method = "spearman",
padj = F,
n = 3)
#> [1] 2 8
#> [1] 3 8
#> [1] 4 8
#> [1] 0
#> [1] 1
#> [1] 2
#> [1] 0
#> [1] 1
#> [1] 0
#> [1] 1
#> [1] 2
p = res[[1]]
p
dat = res[[2]]
head(dat)dat2 = res[[3]]
head(dat2)dat2$m1 = dat2$module1 %>% strsplit("model") %>%
sapply(`[`, 1)
dat2$m2 = dat2$module2 %>% strsplit("model") %>%
sapply(`[`, 1)
dat2$cross = paste(dat2$m1,dat2$m2,sep = "_Vs_")
head(dat2)
p2 = ggplot(dat2) + geom_bar(aes(x = cross,fill = cross)) +
labs(x = "",
y = "numbers.of.similar.modules"
)+ theme_classic()
p2
#--发现分组1和分组3网络更相似一些
FileName <- paste(netpath,"module.compare.groups.pdf", sep = "")
ggsave(FileName, p, width = 10, height = 10)
FileName <- paste(netpath,"numbers.of.similar.modules.pdf", sep = "")
ggsave(FileName, p2, width = 8, height = 8)
FileName <- paste(netpath,"module.otu.csv", sep = "")
write.csv(dat,FileName, quote = F)
FileName <- paste(netpath,"module.compare.groups.csv", sep = "")
write.csv(dat2,FileName, quote = F)
#--鲁棒性
#--随即取出任意比例节点-网络鲁棒性#---------
res = Robustness.Random.removal(ps = ps,
Top = 500,
r.threshold= 0.8,
p.threshold=0.05,
method = "spearman"
)
p = res[[1]]
p
dat = res[[2]]
head(dat)
# dir.create("./Robustness_Random_removal/")
path = paste(netpath,"/Robustness_Random_removal/",sep = "")
fs::dir_create(path)
write.csv(dat,
paste(path,"random_removal_network.csv",sep = ""))
ggsave(paste(path,"random_removal_network.pdf",sep = ""), p,width = 8,height = 4)
res= Robustness.Targeted.removal(ps = ps,
Top = 500,
degree = TRUE,
zipi = FALSE,
r.threshold= 0.8,
p.threshold=0.05,
method = "spearman")
p = res[[1]]
p
dat = res[[2]]
path = paste(netpath,"/Robustness_Targeted_removal/",sep = "")
fs::dir_create(path)
write.csv(dat,
paste(path,"Robustness_Targeted_removal_network.csv",sep = ""))
ggsave(paste(path,"Robustness_Targeted_removal_network.pdf",sep = ""), p,width = 8,height = 4)
#---网络易损性#------
res = Vulnerability.micro(ps = ps,
Top = 500,
degree = TRUE,
zipi = FALSE,
r.threshold= 0.8,
p.threshold=0.05,
method = "spearman")
p = res[[1]] + theme_bw()
p
dat = res[[2]]
path = paste(netpath,"/Vulnerability/",sep = "")
fs::dir_create(path)
write.csv(dat,
paste(path,"Vulnerability_network.csv",sep = ""))
ggsave(paste(path,"Vulnerability_network.pdf",sep = ""), p,width = 4,height = 4)
#--计算负相关的比例#----
res = negative.correlation.ratio(ps = ps,
Top = 500,
degree = TRUE,
zipi = FALSE,
r.threshold= 0.8,
p.threshold=0.05,
method = "spearman")
p = res[[1]]
p
dat = res[[2]]
head(dat)
path = paste(netpath,"/Vulnerability/",sep = "")
fs::dir_create(path)
write.csv(dat,
paste(path,"Vnegative.correlation.ratio_network.csv",sep = ""))
ggsave(paste(path,"negative.correlation.ratio_network.pdf",sep = ""), p,width = 4,height = 4)
#-网络抗毁性#-------
# library(tidyfst)
library("pulsar")
# library(ggClusterNet)
# library(phyloseq)
# library(tidyverse)
res = natural.con.microp (
ps = ps,
Top = 500,
r.threshold= 0.6,
p.threshold=0.05,
method = "spearman",
norm = F,
end = 150,# 小于网络包含的节点数量
start = 0,
con.method = "pulsar"
)
p = res[[1]]
p
dat = res[[2]]
path = paste(netpath,"/Natural_connectivity/",sep = "")
fs::dir_create(path)
write.csv(dat,
paste(path,"/Natural_connectivity.csv",sep = ""))
ggsave(paste(path,"/Natural_connectivity.pdf",sep = ""), p,width = 5,height = 4)network modularity analysis- Module eigenvectors（网络模块化分析-模块特征向量）
id = sample_data(ps)$Group %>% unique()
id
#> [1] "Group1" "Group2" "Group3"
i = 1
netpath = paste(otupath,"/network3_MEs/",sep = "")
dir.create(netpath)
netpath;library(ggClusterNet)
#> [1] ".//result_and_plot//Base_diversity_16s/OTU//network3_MEs/"
library(igraph)
for (i in 1:length(id)) {
ps.1 = phyloseq::subset_samples(
ps,Group %in% c(id[i])
)
result = network.2(ps = ps.1, N = 500,
big = TRUE,
select_layout = TRUE,
layout_net = "model_maptree",
r.threshold=0.8,
p.threshold=0.05,
label = FALSE,
path = netpath,
zipi = F,
ncol = 1,
nrow = 1,
# method = "sparcc",
fill = "Phylum"
)
# 节点的模块化信息的输出 group列为模块化信息
tem <- ggClusterNet::model_maptree(cor =result[[4]],
method = "cluster_fast_greedy",
seed = 12
)
node_model = tem[[2]]
head(node_model)
tablename <- paste(netpath,"/node_model",".csv",sep = "")
write.csv(node_model,tablename)
head(node_model)
otu = ps.1 %>%
filter_OTU_ps(500) %>%
vegan_otu() %>%
as.data.frame()
node_model = node_model[match(colnames(otu),node_model$ID),]
MEList = WGCNA::moduleEigengenes(otu, colors = node_model$group)
MEs = MEList$eigengenes %>% as.data.frame()
tablename <- paste(netpath,"/",id[i],"node_characteristic_variables",".csv",sep = "")
write.csv(MEs,tablename)
}
#> [1] "Group1"
#> [1] "cor matrix culculating over"
#> [1] "Group2"
#> [1] "cor matrix culculating over"
#> [1] "Group3"
#> [1] "cor matrix culculating over"
detach("package:igraph")Comparison and visualization of distances between groups（分组之间距离比较和可视化）
group = "Group"
map = as.data.frame(sample_data(ps))
alppath = paste(otupath,"/distance/",sep = "")
dir.create(alppath)
gro = map[,group] %>% unique()
colnames(gro) = "group"
conbgroup = combn(gro$group,2)
# 计算包括终点均值的所有样品bray距离
bray_curtis = vegan::vegdist(vegan_otu(ps), method = "bray")
bray_curtis = as.matrix(bray_curtis)
for (i in 1:dim(conbgroup)[2]) {
a = conbgroup[,i]
map = as.data.frame(sample_data(ps))
# head(map)
chose1 = map[as.matrix(map[,group]) %>% as.vector() == a[1],] %>% row.names()
chose2 = map[as.matrix(map[,group]) %>% as.vector() == a[2],] %>% row.names()
dat = data.frame(group = paste(a[1],a[2],sep = "_VS_"), Distance =bray_curtis[chose1,chose2] %>% as.dist() %>% as.vector() )
head(dat)
if (i == 1) {
table = dat
}
if (i != 1) {
table = rbind(table,dat)
}
}
head(table) table$id = 1:dim(table)[1]
data <- table %>% dplyr::select(id,everything())
result = EasyStat::MuiKwWlx(data = data,num = c(3))
FileName <- paste(alppath,"/distance_label.csv", sep = "")
write.csv(result,FileName,sep = "")
FileName <- paste(alppath,"/distance_index.csv", sep = "")
write.csv(data,FileName,sep = "")
result1 = EasyStat::FacetMuiPlotresultBox(data = data,num = c(3),result = result,sig_show ="abc",ncol = 1 )
p1_1 = result1[[1]] +
# scale_x_discrete(limits = axis_order) +
mytheme2 +
guides(fill = guide_legend(title = NULL)) +
scale_fill_manual(values = colset4)
p1_1
res = EasyStat::FacetMuiPlotresultBar(data = data,num = c(3),result = result,sig_show ="abc",ncol = 1)
p1_2 = res[[1]]+
# scale_x_discrete(limits = axis_order) +
guides(color = FALSE) +
mytheme2+
guides(fill = guide_legend(title = NULL))+
scale_fill_manual(values = colset4)
p1_2
res = EasyStat::FacetMuiPlotReBoxBar(data = data,num = c(3),result = result,sig_show ="abc",ncol = 1)
p1_3 = res[[1]]+
# scale_x_discrete(limits = axis_order) +
mytheme2 +
guides(fill = guide_legend(title = NULL))+
scale_fill_manual(values = colset4)
p1_3
FileName <- paste(alppath,"distance_box", ".pdf", sep = "")
ggsave(FileName, p1_1, width = ((4+ gnum) ), height =8,limitsize = FALSE)
FileName <- paste(alppath,"distance_bar", ".pdf", sep = "")
ggsave(FileName, p1_2, width = ((4+gnum) ), height = 8,limitsize = FALSE)
FileName <- paste(alppath,"distance_boxbar", ".pdf", sep = "")
ggsave(FileName, p1_3, width = ((4+gnum) ), height = 8,limitsize = FALSE)
FileName <- paste(alppath,"distance_box", ".jpg", sep = "")
ggsave(FileName, p1_1, width = (( 4+gnum) ), height =8,limitsize = FALSE)
FileName <- paste(alppath,"distance_bar", ".jpg", sep = "")
ggsave(FileName, p1_2, width = ((4+ gnum) ), height = 8,limitsize = FALSE)
FileName <- paste(alppath,"distance_boxbar", ".jpg", sep = "")
ggsave(FileName, p1_3, width = ((4+ gnum) ), height = 8,limitsize = FALSE)
5.Function Prediction（5.功能预测）
Tax4Fun2-function prediction（Tax4Fun2-功能预测）
source("./function/runRefBlast2.R")
funcpath = paste(otupath,"/Tax4Fun2/",sep = "")
dir.create(funcpath)
library(Tax4Fun2)
# # 首先配置数据库-可能会经常失败，建议使用这里的备份库
# buildReferenceData(path_to_working_directory = '.', use_force = FALSE, install_suggested_packages = TRUE)
path_to_reference_data = "./Tax4Fun2/Tax4Fun2_ReferenceData_v2"
# blast_bin = file.path(path_to_reference_data, "blast_bin/bin/blastn.exe")
# res = system(command = paste(blast_bin, "-help"), intern = T)
otudir = funcpath
#加载
library(Tax4Fun2)
#物种注释
#指定 OTU 代表序列、Tax4Fun2 库的位置、参考数据库版本、序列比对（blastn）线程数等
runRefBlast2(path_to_otus = './data/otu.fa',
path_to_reference_data = path_to_reference_data,
path_to_temp_folder = otudir, database_mode = 'Ref100NR',
use_force = TRUE, num_threads = 4)
#预测群落功能
#指定 OTU 丰度表、Tax4Fun2 库的位置、参考数据库版本、上步的物种注释结果路径等
makeFunctionalPrediction(path_to_otu_table = './data/otu.txt',
path_to_reference_data = path_to_reference_data,
path_to_temp_folder = otudir,
database_mode = 'Ref100NR',
normalize_by_copy_number = TRUE,
min_identity_to_reference = 0.97,
normalize_pathways = FALSE)Funguild-function prediction（Funguild-功能预测）
# BiocManager::install("microeco")
library(EasyStat)
# 加载R包
library(microeco)
# 加载ggplot2绘图包并设置样式
library(ggplot2)
library("WGCNA")
library(tidyverse)
library(ggtree)
library(SpiecEasi)
library(ggClusterNet)
library(phyloseq)
library(magrittr)
p_list = c("ggplot2", "BiocManager", "devtools","picante", "GUniFrac", "ggalluvial", "rgexf")
for(p in p_list){if (!requireNamespace(p)){install.packages(p)}
library(p, character.only = TRUE, quietly = TRUE, warn.conflicts = F)}
ps = readRDS("./data/dataNEW/ps_ITS.rds")
# ps = ps
# 导入内置真菌数据
# data(sample_info_ITS)
# data(otu_table_ITS)
# data(taxonomy_table_ITS)
otu = ps %>% vegan_otu() %>%
t() %>%
as.data.frame()
tax = ps %>% vegan_tax() %>%
as.data.frame()
# 构建分析对象
dataset = microtable$new(sample_table = sample_data(ps), otu_table = otu, tax_table = tax)
# 筛选真菌
dataset$tax_table %<>% subset(Kingdom == "Fungi")
# 功能预测
t2 = trans_func$new(dataset)
# 计算物种的功能
t2$cal_spe_func()
data = t2$res_spe_func_raw_funguild
fugipath = paste(otupath,"/funguild",sep = "")
dir.create(fugipath)
# dir.create("./result_and_plot/Base_diversity_ITS/OTU_220921//funguild")
write.csv(data,paste(fugipath ,"/funguild.csv",sep = ""))
#----FAPROTAX#---
# install.packages("VGAM")
ps = readRDS("./data/dataNEW/ps_16s.rds")
# 加载R包
library(microeco)
# 加载ggplot2绘图包并设置样式
library(ggplot2)
library("WGCNA")
library(tidyverse)
library(ggtree)
library("SpiecEasi")
library(ggClusterNet)
library(phyloseq)
library(magrittr)
# ps = readRDS("./data/dataNEW/ps_16s.rds")
ps = ps %>%
filter_OTU_ps(500)
otu = ps %>% vegan_otu() %>%
t() %>%
as.data.frame()
tax = ps %>% vegan_tax() %>%
as.data.frame()
# 构建分析对象
dataset = microtable$new(sample_table = sample_data(ps), otu_table = otu, tax_table = tax)
t2 = trans_func$new(dataset)
t2$cal_spe_func()
t2$res_spe_func[1:5, 1:6] data = t2$res_spe_func
data = data[rowSums(data)> 0,]
betapath = paste(otupath,"/FAPROTAX/",sep = "")
dir.create(betapath)
# dir.create("./result_and_plot/Base_diversity_16s//OTU_220921//FAPROTAX")
write.csv(data,paste(betapath,"/FAPROTAX.csv",sep = ""))
# 查看功能 分组列表
t2$func_group_list
#> $`Energy source`
#> [1] "aerobic_chemoheterotrophy" "anaerobic_chemoheterotrophy"
#> [3] "photoautotrophy" "photoheterotrophy"
#>
#> $`C-cycle`
#> [1] "chitinolysis" "cellulolysis" "fermentation" "methanogenesis"
#> [5] "methanotrophy" "methylotrophy"
#>
#> $`N-cycle`
#> [1] "nitrogen_fixation" "aerobic_ammonia_oxidation"
#> [3] "nitrification" "aerobic_nitrite_oxidation"
#> [5] "nitrate_reduction" "nitrate_respiration"
#> [7] "nitrite_respiration"
#>
#> $`S-cycle`
#> [1] "sulfate_respiration" "sulfur_respiration"
#> [3] "sulfite_respiration" "dark_sulfide_oxidation"
#> [5] "respiration_of_sulfur_compounds"
#>
#> $Others
#> [1] "dark_hydrogen_oxidation" "iron_respiration"
#> [3] "manganese_oxidation" "fumarate_respiration"
# 查看某一类
t2$show_prok_func("methanotrophy")
#> [1] "elements:C,H; main_element:C; electron_donor:C; electron_acceptor:variable; aerobic:variable; exclusively_prokaryotic:yes; light_dependent:no"6.Other microbiome analysis（6.其他微生物组分析（环境因子等））
environmental factors and phylogenetic（环境因子和系统发育）
Data Import（导入数据）
env = read.csv("./data/dataNEW/env.csv")
head(env)envRDA = env
head(env)row.names(envRDA) = env$ID
envRDA$ID = NULL
head(envRDA)
res1path <- "./result_and_plot/Micro_and_other_index_16s/"
dir.create(res1path)
res1path
#> [1] "./result_and_plot/Micro_and_other_index_16s/"environmental factors-difference analysis（环境因子-差异分析）
envpath = paste(res1path,"/env_difference_plot/",sep = "")
dir.create(envpath)
map = phyloseq::sample_data(ps)
data = map %>% as.tibble()%>%
dplyr::select(ID,Group) %>%
dplyr::inner_join(env) %>%
as.data.frame() %>%
dplyr::rename(group = Group)
head(data)
result = EasyStat::MuiaovMcomper2(data = data,num = c(3:ncol(data)))
FileName <- paste(envpath,"/env_different_label.csv", sep = "")
write.csv(result,FileName,sep = "")
FileName <- paste(envpath,"/env_group_index.csv", sep = "")
write.csv(data,FileName,sep = "")
result1 = EasyStat::FacetMuiPlotresultBox(data = data,
num = c(3:6),
result = result,
sig_show ="abc",ncol = 5 )
p1_1 = result1[[1]] +
mytheme1 +
ggplot2::guides(fill = guide_legend(title = NULL)) +
ggplot2::scale_fill_manual(values = colset1)
p1_1
p1_1 = result1[[2]] %>% ggplot(aes(x=group , y=dd )) +
geom_violin(alpha=1, aes(fill=group)) +
geom_jitter( aes(color = group),position=position_jitter(0.17), size=3, alpha=0.5)+
labs(x="", y="")+
facet_wrap(.~name,scales="free_y",ncol = 3) +
# theme_classic()+
geom_text(aes(x=group , y=y ,label=stat)) +
# ggplot2::scale_x_discrete(limits = axis_order) +
mytheme1 +
guides(color=guide_legend(title = NULL),
shape=guide_legend(title = NULL),
fill = guide_legend(title = NULL)
) +
ggplot2::scale_fill_manual(values = colset1)
p1_1
res = EasyStat::FacetMuiPlotresultBar(data = data,num = c(3:ncol(data)),result = result,sig_show ="abc",ncol = 5)
p1_2 = res[[1]]+
# scale_x_discrete(limits = axis_order) +
guides(color = FALSE) +
mytheme1+
guides(fill = guide_legend(title = NULL))+
scale_fill_manual(values = colset1)
p1_2
res = EasyStat::FacetMuiPlotReBoxBar(data = data,num = c(3:ncol(data)),result = result,sig_show ="abc",ncol = 5)
p1_3 = res[[1]]+
# scale_x_discrete(limits = axis_order) +
mytheme1 +
guides(fill = guide_legend(title = NULL))+
scale_fill_manual(values = colset1)
p1_3
FileName <- paste(envpath,"env_Facet_box", ".pdf", sep = "")
ggsave(FileName, p1_1, width = ((1 + gnum) * 3), height =4*gnum,limitsize = FALSE)
FileName <- paste(envpath,"env_Facet_bar", ".pdf", sep = "")
ggsave(FileName, p1_2, width = ((1 + gnum) * 3), height = 4*gnum,limitsize = FALSE)
FileName <- paste(envpath,"env_Facet_boxbar", ".pdf", sep = "")
ggsave(FileName, p1_3, width = ((1 + gnum) * 3), height = 4*gnum,limitsize = FALSE)
FileName <- paste(envpath,"env_Facet_box", ".jpg", sep = "")
ggsave(FileName, p1_1, width = ((1 + gnum) * 3), height =4*gnum,limitsize = FALSE)
FileName <- paste(envpath,"env_Facet_bar", ".jpg", sep = "")
ggsave(FileName, p1_2, width = ((1 + gnum) * 3), height = 4*gnum,limitsize = FALSE)
FileName <- paste(envpath,"env_Facet_boxbar", ".jpg", sep = "")
ggsave(FileName, p1_3, width = ((1 + gnum) * 3), height = 4*gnum,limitsize = FALSE)environmental factors-correlation analysis（环境因子相关分析）
library(igraph)
library(network)
library(sna)
corpath = paste(res1path,"/env_self_cor/",sep = "")
dir.create(corpath)
ps_env = phyloseq::phyloseq(phyloseq::otu_table(as.matrix(envRDA),taxa_are_rows = F),
phyloseq::sample_data(ps)
)
result = ggClusterNet::corMicro (ps = ps_env,
N = 0,
r.threshold=0.9,
p.threshold=0.05
)
cor = result[[1]]
dim(cor)
#> [1] 34 34
ps_net = result[[3]]
otu_table = as.data.frame(t(ggClusterNet::vegan_otu(ps_net)))
# tax_table = as.data.frame(ggClusterNet::vegan_tax(ps_net))
netClu = data.frame(ID = row.names(cor),group = "one")
head(netClu)netClu$group = "one"
set.seed(12)
result2 = ggClusterNet::PolygonClusterG(cor = cor,node = netClu )
node = result2[[1]]
edge = ggClusterNet::edgeBuild(cor = cor,node = node)
colnames(edge)[8] = "cor"
p1 <- ggplot() + geom_segment(aes(x = X1, y = Y1, xend = X2, yend = Y2,color = cor ),
data = edge, size = 0.5,alpha = 0.6) +
geom_point(aes(X1, X2),pch = 21, data = node, size = 3,fill = "#9ACD32") +
ggrepel::geom_text_repel(aes(X1, X2,label = elements),pch = 21,data = node) +
# geom_text(aes(X1, X2,label = elements),pch = 21, data = nodes) +
scale_colour_manual(values = c("#377EB8","#E41A1C")) +
scale_size(range = c(4, 14)) +
scale_x_continuous(breaks = NULL) + scale_y_continuous(breaks = NULL) +
theme(panel.background = element_blank()) +
theme(axis.title.x = element_blank(), axis.title.y = element_blank()) +
theme(legend.background = element_rect(colour = NA)) +
theme(panel.background = element_rect(fill = "white", colour = NA)) +
theme(panel.grid.minor = element_blank(), panel.grid.major = element_blank())
p1
FileName <- paste(corpath,"env_self_cor", ".pdf", sep = "")
ggsave(FileName, p1, width = 10, height =9,limitsize = FALSE)
FileName <- paste(corpath,"env_self_cor", ".png", sep = "")
ggsave(FileName, p1, width = 10, height = 9,limitsize = FALSE)Microbial and environmental factors - co-ordination_RDA/CCA（微生物和环境因子共排序RDA/CCA）
ps = readRDS("./data/dataNEW/ps_16s.rds")
RDApath = paste(res1path,"/RDA_CCA/",sep = "")
dir.create(RDApath)
source("./function/rda-cca.R ")
ps.r = ps %>% filter_OTU_ps(500)
result = RDA_CCA(ps = ps.r,
env = envRDA,
path = RDApath,
chose.env = FALSE
)
#> List of 14
#> $ species : num [1:500, 1:6] 0.000538 -0.002618 -0.005316 -0.00213 -0.000955 ...
#> ..- attr(*, "dimnames")=List of 2
#> .. ..$ : chr [1:500] "ASV_662" "ASV_782" "ASV_874" "ASV_223" ...
#> .. ..$ : chr [1:6] "RDA1" "RDA2" "RDA3" "RDA4" ...
#> $ sites : num [1:18, 1:6] -0.0286 0.0763 -0.0393 0.0462 -0.0493 ...
#> ..- attr(*, "dimnames")=List of 2
#> .. ..$ : chr [1:18] "sample1" "sample10" "sample11" "sample12" ...
#> .. ..$ : chr [1:6] "RDA1" "RDA2" "RDA3" "RDA4" ...
#> $ constraints: num [1:18, 1:6] -0.0286 0.0763 -0.0393 0.0462 -0.0493 ...
#> ..- attr(*, "dimnames")=List of 2
#> .. ..$ : chr [1:18] "sample1" "sample10" "sample11" "sample12" ...
#> .. ..$ : chr [1:6] "RDA1" "RDA2" "RDA3" "RDA4" ...
#> $ biplot : num [1:17, 1:6] -0.404 -0.2555 -0.2705 -0.1101 -0.0634 ...
#> ..- attr(*, "dimnames")=List of 2
#> .. ..$ : chr [1:17] "pH" "SOC" "TN" "NH4.N" ...
#> .. ..$ : chr [1:6] "RDA1" "RDA2" "RDA3" "RDA4" ...
#> $ call : language rda(X = otu, Y = env.st2)
#> $ tot.chi : num 0.00387
#> $ constr.chi : num 0.00387
#> $ unconst.chi: num 0
#> $ cont :List of 1
#> ..$ importance: 'summary.eigenvals' num [1:3, 1:17] 0.002462 0.636304 0.636304 0.000496 0.128087 ...
#> .. ..- attr(*, "dimnames")=List of 2
#> .. .. ..$ : chr [1:3] "Eigenvalue" "Proportion Explained" "Cumulative Proportion"
#> .. .. ..$ : chr [1:17] "RDA1" "RDA2" "RDA3" "RDA4" ...
#> $ concont :List of 1
#> ..$ importance: 'summary.eigenvals' num [1:3, 1:17] 0.002462 0.636304 0.636304 0.000496 0.128087 ...
#> .. ..- attr(*, "dimnames")=List of 2
#> .. .. ..$ : chr [1:3] "Eigenvalue" "Proportion Explained" "Cumulative Proportion"
#> .. .. ..$ : chr [1:17] "RDA1" "RDA2" "RDA3" "RDA4" ...
#> $ scaling : num 2
#> $ digits : num 4
#> $ inertia : chr "variance"
#> $ method : chr "rda"
#> - attr(*, "const")= num 0.506
#> - attr(*, "class")= chr "summary.cca"
#提取图片
p1 = result[[1]] + mytheme1 + scale_fill_manual(values = colset1)
p1
# 提取作图数据
dataplot = result[[2]]
# 提取带有标记的图片
p2 = result[[3]]+ mytheme1 + scale_fill_manual(values = colset1)
#提取理化提供群落关系的渐检验结果
aov = result[[4]]
# library("Cairo")
##保存
plotnamea = paste(RDApath,"/RDA_envnew.pdf",sep = "")
ggsave(plotnamea, p1, width = 8, height = 6)
plotnamea4 = paste(RDApath,"/RDA_envnew.jpg",sep = "")
ggsave(plotnamea4, p1, width = 8, height = 6)
filenamea = paste(RDApath,"dataplotnew.txt",sep = "")
write.table(dataplot ,file=filenamea,sep="\t",col.names=NA)
filenamea = paste(RDApath,"aovnew.txt",sep = "")
write.table(aov,file=filenamea,sep="\t",col.names=NA)
plotnamea = paste(RDApath,"/RDA_envlabelnew.pdf",sep = "")
ggsave(plotnamea, p2, width = 18, height = 12)#, device = cairo_pdf, family = "Song"
plotnamea4 = paste(RDApath,"/RDA_envlabelnew.png",sep = "")
ggsave(plotnamea4, p2, width = 18, height = 12)# , device = cairo_pdf, family = "Song"
result = RDA_CCA_explain_percent(ps = ps.r,
env.dat = envRDA)
out = result[[1]]
wxp = result[[2]]
filenamea = paste(RDApath,"each_env_exp_percent.csv",sep = "")
write.csv(out,filenamea)
filenamea = paste(RDApath,"all_index_explain_percent.csv",sep = "")
write.csv(exp,filenamea)Hierarchical segmentation RDA/CCA（层次分割RDA/CCA）
# BiocManager::install("rdacca.hp")
library(rdacca.hp)
library(vegan)
library(ggClusterNet)
library(tidyverse)
hpPath = paste(res1path,"/rdacca.hp/",sep = "")
dir.create(hpPath)
source("./function/rdacca.hp.micro.p.R")
rdacca.hp.micro.p(
OTU = ps %>% filter_OTU_ps(200) %>%vegan_otu() %>% as.data.frame(),
env = envRDA[,1:5],
hpPath = hpPath,
cca = FALSE,
dbRDA = FALSE
)
#>
#> Please wait: running 8 permutationsnon-restricted cluster analysis based on dmm - search for community response factors（基于dmm进行非限制性聚类分析-寻找群落响应的因子）
library(dplyr)
library(microbiome)
library(DirichletMultinomial)
library(reshape2)
library(magrittr)
library(ggClusterNet)
ps = readRDS("./data/dataNEW/ps_16s.rds")
ps.1 = ps %>% filter_OTU_ps(200)
dmmPath = paste(res1path,"/dmm/",sep = "")
dir.create(dmmPath)
num = 3
path = dmmPath
dat<-abundances(ps.1)#222个样本中核心物种的丰富度，行为种名，列为样本对应丰富度
count<-as.matrix(t(dat))#将核心群的丰度数据转置且变为矩阵，行为样本数，列为不同的种名
fit<-lapply(1:num,dmn,count=count,verbose=TRUE)#lapply函数调用函数dmn，对样本计数矩阵拟合多项式模型
#> Soft kmeans
#> Expectation Maximization setup
#> Expectation Maximization
#> Hessian
#> Soft kmeans
#> iteration 10 change 0.003635
#> iteration 20 change 0.000599
#> iteration 30 change 0.000236
#> iteration 40 change 0.000118
#> iteration 50 change 0.000066
#> iteration 60 change 0.000039
#> iteration 70 change 0.000023
#> iteration 80 change 0.000014
#> iteration 90 change 0.000009
#> iteration 100 change 0.000006
#> iteration 110 change 0.000004
#> iteration 120 change 0.000002
#> iteration 130 change 0.000001
#> Expectation Maximization setup
#> Expectation Maximization
#> Hessian
#> Soft kmeans
#> iteration 10 change 0.004656
#> iteration 20 change 0.000019
#> iteration 30 change 0.000009
#> iteration 40 change 0.000004
#> iteration 50 change 0.000002
#> Expectation Maximization setup
#> Expectation Maximization
#> Hessian
#群落类型数设为3，得到三个dmn，
#判断拟合效果
lplc<-sapply(fit,laplace)#laplace获取拟合的模型的参数
aic<-sapply(fit,AIC)
bic<-sapply(fit,BIC)#用最大似然法估计参数时，AIC、BIC越小拟合的越好
#选择最佳模型
best<-fit[[which.min(unlist(lplc))]]#unlist对所有参数列表的模型挑选参数最小的
mixturewt(best)#获取最佳拟合模型的参数pi和theta值
# 保存最佳模型
ass<-apply(mixture(best),1,which.max)
FileName <- paste(dmmPath,"amm_best_model", ".csv", sep = "")
write.csv(ass, file=FileName)
for(k in seq(ncol(fitted(best)))){
d<-melt(fitted(best))#melt是数据整合函数宽格式转化为长格式,对数据进行重塑
colnames(d)<-c("OTU","cluster","value")#重塑后数据列名设置
d <- subset(d, cluster == k) %>%
# Arrange OTUs by assignment strength
arrange(value) %>%
mutate(OTU = factor(OTU, levels = unique(OTU))) %>%
#mutate函数在数据框中添加新变量，unique函数去除重复值提取唯一值，并将对应OTU转化为因子，只展示最重要的
filter(abs(value) > quantile(abs(value), 0.8)) #过滤 OTUvalue值大于四分位数的
p <- ggplot(d, aes(x = OTU, y = value)) +
geom_bar(stat = "identity",fill="#9ACD32") +
coord_flip() + #坐标轴转换
labs(title = paste("Top drivers: community type", k)) +
theme_classic()
filename = paste(dmmPath,k,'_important.pdf',sep = "")
ggsave(filename,p)
filename = paste(dmmPath,'_important.jpg',sep = "")
ggsave(filename,p)
}
Microbial communities + Science article- Chart combination（微生物群落和环境Science组合图表）
ps = base::readRDS("./data/dataNEW/ps_16s.rds")
env = read.csv("./data/dataNEW/env.csv")
head(env)envRDA = env
head(env)row.names(envRDA) = env$ID
envRDA$ID = NULL
head(envRDA)
compath = paste(res1path,"/Conbine_env_plot/",sep = "")
dir.create(compath)
otu1 = as.data.frame(t(ggClusterNet::vegan_otu(ps)))
head(otu1)
tabOTU1 = list(bac = otu1)
# tabOTU1 = list(b = otu)
# tabOTU1 = list(f = otu2)
p0 <- ggClusterNet:: MatCorPlot(env.dat = envRDA,
tabOTU = tabOTU1,
diag = FALSE,
range = 0.1,
numpoint = 21,
sig = TRUE,
siglabel = FALSE,
shownum = FALSE,
curvature = 0.1,
numsymbol = NULL,
lacx = "left",
lacy = "bottom",
p.thur = 0.05,
onlysig = TRUE
)
p0
p0 <- p0 + scale_colour_manual(values = c("blue","red")) +
scale_fill_distiller(palette="PRGn")
p0
FileName <- paste(compath,"Conbine_envplot", ".pdf", sep = "")
ggsave(FileName, p0,width = 15,height = 10)# , device = cairo_pdf, family = "Song"
FileName <- paste(compath,"Conbine_envplot", ".png", sep = "")
ggsave(FileName, p0,width = 15,height = 10)# , device = cairo_pdf, family = "Song"
#--拆分开来
#--- mantel test
rep = ggClusterNet::MetalTast (env.dat = envRDA, tabOTU = tabOTU1,distance = "bray",method = "metal")
repR = rep[c(-seq(from=1,to=dim(rep)[2],by=2)[-1])]
repP = rep[seq(from=1,to=dim(rep)[2],by=2)]
head( repR)head( repP)mat = cbind(repR,repP)
head(mat)
FileName <- paste(compath,"Conbine_envplot_data", ".csv", sep = "")
write.csv(mat,FileName)
result <- ggClusterNet::Miccorplot(data = envRDA,
method.cor = "spearman",
cor.p = 0.05,
x = F,
y = F,
diag = T,
lacx = "left",
lacy = "bottom",
sig = T,
siglabel = F,
shownum = F,
numpoint = 21,
numsymbol = NULL
)
p1 <- result[[1]]
p1 <- p1 + scale_fill_distiller(palette="PRGn")
p1
FileName <- paste(compath,"envCorplot", ".pdf", sep = "")
ggsave(FileName, p1,width = 15,height = 10)
FileName <- paste(compath,"envCorplot", ".png", sep = "")
ggsave(FileName, p1,width = 15,height = 10)The important indexes of random fores regression（随机森林回归最重要的指标）
# BiocManager::install("rfPermute")
#--随机森林回归最重要的指标#--------
rampath = paste(res1path,"/Random_env/",sep = "")
dir.create(rampath)
ps = ps
env = envRDA
seed = 1
otu<- as.data.frame(t(ggClusterNet::vegan_otu(ps)))
map <- design <- phyloseq::sample_data(ps)
aa<-lapply(otu, function(x) as.numeric(as.character(x)))
aa<-as.data.frame(aa)
otu<-aa
dist= vegan::vegdist(t(otu), method="bray")
nmds= vegan::monoMDS(dist)
index = as.data.frame(nmds$points)
index<-cbind(design,index)
NMDS1<- cbind(env,MDS1 = index$MDS1)
set.seed(seed)
bb= randomForest::randomForest(NMDS1$MDS1 ~., data=NMDS1,importance = TRUE, ntree = 500, nrep = 100, na.action=na.omit)
bb
#>
#> Call:
#> randomForest(formula = NMDS1$MDS1 ~ ., data = NMDS1, importance = TRUE, ntree = 500, nrep = 100, na.action = na.omit)
#> Type of random forest: regression
#> Number of trees: 500
#> No. of variables tried at each split: 11
#>
#> Mean of squared residuals: 0.8810731
#> % Var explained: -23.58
df<-round(randomForest::importance(bb), 2)
df<-as.data.frame(df)
df<-cbind(df,rownames(df))
p <- ggplot(df, aes(x =`%IncMSE` , y =reorder(`rownames(df)`,`%IncMSE`) )) +
geom_bar(stat = "identity", width = 0.75,position = "dodge",colour="black",fill="#9ACD32",alpha=1) +
labs(y="", x="", title = "",size=9)+
theme_bw() +
theme(axis.text=element_text(colour='black',size=9))
p
data = df
head(data)hit <- dim(envRDA)[2]
hit
#> [1] 34
FileName <- paste(rampath,"ranImportant", ".pdf", sep = "")
ggsave(FileName, p,width = 6,height =hit/5)
FileName <- paste(rampath,"ranImportant", ".csv", sep = "")
write.csv(data,FileName)
#--分组多轴随机森林#---------
ramdom_env_micro.heatplot <- function(
ps = ps,
env = env,
seed = 1
){
map = phyloseq::sample_data(ps)
x = unique(map$Group) %>% length()
for (j in 1:x) {
pssub = ps
map.1 = map[map$Group == unique(map$Group)[j],]
phyloseq::sample_data(pssub) = map
dat <- random.cor(pssub = pssub,seed = seed)
head(dat)
dat = cbind(dat,facet = unique(map$Group)[j])
if (j == 1) {
dat0 = dat
}
if (j != 1) {
dat0 = rbind(dat0,dat)
}
}
head(dat0)
df = dat0[,c(1,3,5,8)]
head(df)
pcm = reshape2::melt(df, id = c("id","facet"))
head(pcm)
df = dat0[,c(5,6,7,8)]
head(df)
colnames(df)[c(2,3)] = c("PC1","PC2")
pcm1 = reshape2::melt(df, id = c("id","facet"))
head(pcm1)
colnames(pcm1)[4] = "label"
pcm2 = pcm %>%
dplyr::inner_join(pcm1)
p = ggplot(pcm2, aes(y = id, x = variable)) +
# geom_point(aes(size = value,fill = value), alpha = 0.75, shape = 21) +
geom_tile(aes(size = value,fill = value))+
scale_size_continuous(limits = c(0.000001, 100), range = c(2,25), breaks = c(0.1,0.5,1),guide = F) +
geom_text(aes(label = label)) +
labs( y= "", x = "", size = "", fill = "") +
# scale_fill_manual(values = colours, guide = FALSE) +
scale_x_discrete(limits = rev(levels(pcm$variable))) +
scale_x_discrete(limits = c("PC1","PC2")) +
# scale_y_discrete(position = "right") +
scale_fill_gradientn(colours =colorRampPalette(c("white","#984EA3"))(60)) +
theme(
panel.background=element_blank(),
panel.grid=element_blank(),
axis.text.x = element_text(colour = "black",size = 5,angle = 60,vjust = 1,hjust = 1)
) +facet_wrap(.~facet,scales="free_y",ncol = x)
p
# p <- ggplot(df, aes(x =`%IncMSE` , y =reorder(`rownames(df)`,`%IncMSE`) )) +
# geom_bar(stat = "identity", width = 0.75,position = "dodge",colour="black",fill="#9ACD32",alpha=1) +
# geom_text(aes(label = label),size = 5) +
# labs(y="", x="", title = "",size=9)+
# theme_bw() +
# theme(axis.text=element_text(colour='black',size=9))
# p
return(list(p,pcm2))
}
# dat0$label.PC1
# dat0$PC1.p
random.cor = function(pssub = ps,seed = seed){
otu<- as.data.frame(t( ggClusterNet::vegan_otu(pssub)))
map <- design <- phyloseq::sample_data(pssub)
aa<-lapply(otu, function(x) as.numeric(as.character(x)))
aa<-as.data.frame(aa)
otu<-aa
dist= vegan::vegdist(t(otu), method="bray")
nmds=stats::prcomp(dist)
index = as.data.frame(nmds$x)
index<-cbind(design,index)
head(index)
NMDS1<- cbind(env,MDS1 = index$PC1)
set.seed(seed)
# bb= randomForest::randomForest(NMDS1$MDS1 ~., data=NMDS1,importance = TRUE, ntree = 500, nrep = 100, na.action=na.omit)
# library(rfPermute)
# library(rfUtilities)
frichness.rfP <- rfPermute::rfPermute(NMDS1$MDS1 ~., data=NMDS1, ntree = 500, na.action = na.omit, nrep = 100, num.cores = 1)
frimp.scaled1 <- rfPermute::importance(frichness.rfP, scale = TRUE)%>% round(3)
frimp.scaled1 <- frimp.scaled1[,1:2]
colnames(frimp.scaled1) = c("PC1","PC1.p")
NMDS1<- cbind(env,MDS1 = index$PC2)
frichness.rfP <- rfPermute::rfPermute(NMDS1$MDS1 ~., data=NMDS1, ntree = 500, na.action = na.omit, nrep = 100, num.cores = 1)
frimp.scaled2 <- rfPermute::importance(frichness.rfP, scale = TRUE) %>% round(3)
frimp.scaled2 <- frimp.scaled2[,1:2]
colnames(frimp.scaled2 ) = c("PC2","PC2.p")
frimp.scaled1[is.na(frimp.scaled1)] = 1
frimp.scaled2[is.na(frimp.scaled2)] = 1
dat = cbind(frimp.scaled1,frimp.scaled2) %>% as.data.frame()
df<-cbind(dat,id = rownames(dat))
head(df)
A = c()
for (i in 1:length(df$PC1.p)) {
if (df$PC1.p [i] < 0.05) {
A[i] = "*"
} else {
A[i] = ""
}
}
df$label.PC1 = A
A = c()
for (i in 1:length(df$PC2.p)) {
if (df$PC2.p [i] < 0.05) {
A[i] = "*"
} else {
A[i] = ""
}
}
df$label.PC2 = A
head(df)
return(df)
}
rampath = paste(res1path,"/Random_env/",sep = "")
dir.create(rampath)
# ps = ps0
# map = sample_data(ps)
result <- ramdom_env_micro.heatplot( ps = ps, env = env,seed = 1)
p <- result[[1]] + mytheme2
p
dat = result[[2]]
wid = dim(env)[2]
map = phyloseq::sample_data(ps)
hei = length(unique(map$Group))
hei
#> [1] 3
FileName <- paste(rampath,"Randomforest_env_micro_heatmap", ".pdf", sep = "")
ggsave(FileName, p,width = 8 ,height = wid/4)
FileName <- paste(rampath,"Randomforest_env_micro_heatmap", ".jpg", sep = "")
ggsave(FileName, p,width = 8 ,height = wid/4)
FileName <- paste(rampath,"Random_env_micro_heatmap", ".csv", sep = "")
write.csv(dat,FileName)The relationships between environmental factors and high-abundance microorganisms（高丰度微生物和环境因子的关系探索）
cor_env_ggcorplot <- function(
env1 = env1,
env2 = env2,
label = T,
col_cluster = T,
row_cluster = T,
method = "spearman",
r.threshold=0.6,
p.threshold=0.05,
theme.size = 10
){
if (dim(env2)[2] == 1) {
env2 = env2
} else {
env2 <- env2[match(row.names(env1),row.names(env2)),]
}
env0 <- cbind(env1,env2)
occor = psych::corr.test(env0,use="pairwise",method=method,adjust="fdr",alpha=.05)
occor.r = occor$r
occor.p = occor$p
occor.r[occor.p > p.threshold&abs(occor.r) < r.threshold] = 0
head(env0)
# data[data > 0.3]<-0.3
#drop gene column as now in rows
if (col_cluster) {
clust <- hclust(dist(env1 %>% as.matrix()%>% t())) # hclust with distance matrix
ggtree_plot <- ggtree::ggtree(clust)
}
if (row_cluster) {
v_clust <- hclust(dist(env2 %>% as.matrix() %>% t()))
ggtree_plot_col <- ggtree::ggtree(v_clust) + ggtree::layout_dendrogram()
}
occor.r = as.data.frame(occor.r)
if (dim(env2)[2] == 1) {
data <- occor.r[colnames(env1),colnames(env2)]
data = data.frame(row.names = colnames(env1),data)
colnames(data) = colnames(env2)
data$id = row.names(data)
} else {
data <- occor.r[colnames(env1),colnames(env2)]
data$id = row.names(data)
}
pcm = reshape2::melt(data, id = c("id"))
head(pcm)
occor.p = as.data.frame(occor.p)
if (dim(env2)[2] == 1) {
data <- occor.p[colnames(env1),colnames(env2)]
data = data.frame(row.names = colnames(env1),data)
colnames(data) = colnames(env2)
data$id = row.names(data)
} else {
data <- occor.p[colnames(env1),colnames(env2)]
data$id = row.names(data)
}
pcm2 = reshape2::melt(data, id = c("id"))
head(pcm2)
colnames(pcm2)[3] = "p"
pcm2$lab = pcm2$p
pcm2$lab[pcm2$lab < 0.001] = "**"
pcm2$lab[pcm2$lab < 0.05] = "*"
pcm2$lab[pcm2$lab >= 0.05] = ""
pcm3 = pcm %>% left_join(pcm2)
p1 = ggplot(pcm3, aes(y = id, x = variable)) +
# geom_point(aes(size = value,fill = value), alpha = 0.75, shape = 21) +
geom_tile(aes(size = value,fill = value))+
scale_size_continuous(limits = c(0.000001, 100), range = c(2,25), breaks = c(0.1,0.5,1)) +
geom_text(aes(label = lab)) +
labs( y= "", x = "", size = "Relative Abundance (%)", fill = "") +
# scale_fill_manual(values = colours, guide = FALSE) +
scale_x_discrete(limits = rev(levels(pcm$variable))) +
scale_y_discrete(position = "right") +
scale_fill_gradientn(colours =colorRampPalette(c("#377EB8","#F7F4F9","#E41A1C"))(60)) +
theme(
panel.background=element_blank(),
panel.grid=element_blank(),
axis.text.x = element_text(colour = "black",size = theme.size,angle = 60,vjust = 1,hjust = 1)
)
p2 = ggplot(pcm3, aes(y = id, x = variable)) +
geom_point(aes(size = value,fill = value), alpha = 0.75, shape = 21) +
scale_size_continuous(limits = c(0.000001, 100), range = c(2,25), breaks = c(0.1,0.5,1)) +
geom_text(aes(label = lab)) +
labs( y= "", x = "", size = "Relative Abundance (%)", fill = "") +
# scale_fill_manual(values = colours, guide = FALSE) +
scale_x_discrete(limits = rev(levels(pcm$variable))) +
scale_y_discrete(position = "right") +
scale_fill_gradientn(colours =colorRampPalette(c("#377EB8","#F7F4F9","#E41A1C"))(60)) +
theme(
panel.background=element_blank(),
panel.grid=element_blank(),
axis.text.x = element_text(colour = "black",size = theme.size,angle = 60,vjust = 1,hjust = 1)
)
if (col_cluster) {
p1 <- p1 %>%
aplot::insert_left(ggtree_plot, width=.2)
p2 <- p2 %>%
aplot::insert_left(ggtree_plot, width=.2)
}
if (label) {
p1 <- p1 %>%
aplot::insert_top(ggtree_plot_col, height=.1)
p2 <- p2 %>%
aplot::insert_top(ggtree_plot_col, height=.1)
}
return(list(p1,p2))
}
heatpath = paste(res1path,"/cor_env_heapmap_boplot/",sep = "")
dir.create(heatpath)
# library(sna)
# library(igraph)
#---提取门水平
jj = 6
tran = TRUE
Top = 20
for (jj in 2:6) {
psdata <- ggClusterNet::tax_glom_wt(ps = ps,ranks = jj)
if (tran) {
psdata = psdata %>%
phyloseq::transform_sample_counts(function(x) {x/sum(x)})
}
otu = phyloseq::otu_table(psdata)
tax = phyloseq::tax_table(psdata)
if (dim(otu)[1] < Top) {
top10 <- otu[names(sort(rowSums(otu), decreasing = TRUE)[1:dim(otu)[1]]),]
top10 = t(top10)
} else {
top10 <- otu[names(sort(rowSums(otu), decreasing = TRUE)[1:Top]),]
top10 = t(top10)
}
head(top10)
result = cor_env_ggcorplot(
env1 = envRDA,
env2 = top10,
label = TRUE,
col_cluster = TRUE,
row_cluster = TRUE,
method = "spearman",
r.threshold= 0,
p.threshold= 0
)
p1 <- result[[1]]
p1
p2 <- result[[2]]
p2
hei = dim(env)[2]/5
wid = Top
filename = paste(heatpath,Top,"_",jj,"_","abundacne_OTU.csv",sep = "")
write.csv(top10,filename)
filename = paste(heatpath,"/",jj,"ggheatmap.pdf",sep = "")
ggsave(filename,p1,width = Top/2,height = dim(env)[2]/5)
filename = paste(heatpath,"/",jj,"ggbubble.pdf",sep = "")
ggsave(filename,p2,width = Top/2,height = dim(env)[2]/5)
filename = paste(heatpath,"/",jj,"ggheatmap.jpg",sep = "")
ggsave(filename,p1,width = Top/2,height = dim(env)[2]/5)
filename = paste(heatpath,"/",jj,"ggbubble.jpg",sep = "")
ggsave(filename,p2,width = Top/2,height = dim(env)[2]/5)
}Exploring the relationship between the large magnitude of change microorganisms and environmental factors（变化幅度大的微生物和环境因子的关系探索）
heatpath = paste(res1path,"/CVmicro_env_hea_boplot/",sep = "")
dir.create(heatpath)
# library(sna)
# library(igraph)
#---提取门水平
tran = TRUE
Top = 40
for (j in 2:6) {
ps_tem = ps %>%
ggClusterNet::scale_micro(method = "rela") %>%
ggClusterNet::tax_glom_wt(ranks = j)
rowSD = function(x){
apply(x,1, sd)
}
rowCV = function(x){
rowSD(x)/rowMeans(x)
}
id <- ps %>%
ggClusterNet::scale_micro(method = "rela") %>%
ggClusterNet::tax_glom_wt(ranks = j) %>%
# ggClusterNet::filter_OTU_ps(100) %>%
ggClusterNet::vegan_otu() %>%
t() %>% as.data.frame() %>%
rowCV %>%
sort(decreasing = TRUE) %>%
head(Top) %>%
names()
otu = phyloseq::otu_table(ps_tem)
tax = phyloseq::tax_table(ps_tem)
head(otu)
data = otu[id,] %>% t() %>%
as.data.frame()
result = cor_env_ggcorplot(
env1 = envRDA,
env2 = data,
label = TRUE,
col_cluster = TRUE,
row_cluster = TRUE,
method = "spearman",
r.threshold= 0,
p.threshold= 0
)
p1 <- result[[1]]
p1
p2 <- result[[2]]
p2
hei = dim(env)[2]/5
wid = Top
filename = paste(heatpath,Top,"_",j,"_","abundacne_OTU.csv",sep = "")
write.csv(data,filename)
filename = paste(heatpath,"/",j,"ggheatmap.pdf",sep = "")
ggsave(filename,p1,width = Top/2,height = dim(env)[2]/5)
filename = paste(heatpath,"/",jj,"ggbubble.pdf",sep = "")
ggsave(filename,p2,width = Top/2,height = dim(env)[2]/5)
filename = paste(heatpath,"/",j,"ggheatmap.jpg",sep = "")
ggsave(filename,p1,width = Top/2,height = dim(env)[2]/5)
filename = paste(heatpath,"/",j,"ggbubble.jpg",sep = "")
ggsave(filename,p2,width = Top/2,height = dim(env)[2]/5)
}Analysis of the relationship between network modules and environmental factors（基于网络模块和环境因子的关系分析）
library(phyloseq)
library(ggClusterNet)
library(tidyverse)
env = read.csv("./data/dataNEW/env.csv")
head(env)envRDA = env
head(env)row.names(envRDA) = env$ID
envRDA$ID = NULL
head(envRDA)ps = readRDS("./data/dataNEW/ps_16s.rds")
id = sample_data(ps)$Group %>% unique()
id
#> [1] "Group1" "Group2" "Group3"
i = 1
for (i in 1:length(id)) {
netpath = paste(res1path,"/network_env_hub_",id[i],"/",sep = "")
dir.create(netpath)
# ps.1 = phyloseq::subset_samples(
# ps,Group %in% c(id[i])
# )
ps.1 = ps %>% scale_micro("TMM") %>%
subset_samples(
Group %in% c(id[i])
)
library(ggClusterNet)
library(igraph)
#--计算微生物网络相关矩阵
result= ggClusterNet::cor_Big_micro(ps = ps.1,
N = 500,
p.threshold = 0.05,
r.threshold = 0.8,
scale = FALSE)
cor = result[[1]]
#--拟合模块
tem <- model_maptree(cor =result[[1]],
method = "cluster_fast_greedy",
seed = 12
)
node_model = tem[[2]]
head(node_model)
otu = ps.1 %>%
phyloseq::subset_taxa(
row.names(tax_table(ps ))%in%c(row.names(result[[1]]))) %>%
vegan_otu() %>%
as.data.frame()
#-对其
node_model = node_model[match(colnames(otu),node_model$ID),]
MEList = WGCNA::moduleEigengenes(otu, colors = node_model$group)
MEs = MEList$eigengenes
tablename <- paste(netpath,"/model_network_feature_value_",id[i],".csv",sep = "")
write.csv(MEs,tablename)
#--寻找对于某个环境因子作用最大的模块
ramdom_Model_env.plot <- function(
model = model,
sink = env,
seed = 1
){
model$ID = row.names(model)
set.seed(seed)
tem.r = sink %>% inner_join(model,by = "ID") %>%
select(-ID)
frichness.rfP <- rfPermute::rfPermute(tem.r[[1]] ~., data=tem.r[,-1],
ntree = 500, na.action = na.omit, nrep = 100, num.cores = 1)
frimp.scaled1 <- rfPermute::importance(frichness.rfP, scale = TRUE)%>% round(3)
frimp.scaled1 <- frimp.scaled1[,1:2]
df<-cbind(as.data.frame(frimp.scaled1),rownames(frimp.scaled1))
head(df)
df$`%IncMSE.pval`[is.na(df$`%IncMSE.pval`)] = 1
i = 1
a = c()
for (i in 1:length(df$`%IncMSE.pval`)) {
if (df$`%IncMSE.pval`[i] > 0.05) {
a[i] = ""
} else {
a[i] = "*"
}
}
df$lab = a
df$`%IncMSE`[is.na(df$`%IncMSE`)] = 0
p <- ggplot(df, aes(x =`%IncMSE` , y =reorder(`rownames(frimp.scaled1)`,`%IncMSE`) )) +
geom_bar(stat = "identity", width = 0.75,position = "dodge",colour="black",fill="#9ACD32",alpha=1) +
geom_text(aes(label = lab),hjust = -1) +
labs(y="Model in network", x="%IncMSE", title = colnames(sink)[2],size=9)+
theme_bw() +
theme(axis.text=element_text(colour='black',size=9))
return(list(df,p))
}
env.1 = env %>% filter(ID %in% sample_names(ps.1))
result <- ramdom_Model_env.plot(model = MEs,
sink =env.1[,1:2] )
p <- result[[2]]
p
data = result[[1]]
head(data)
hit <- dim(MEs)[2]
hit
FileName <- paste(netpath,"/ranImportant_Model_order_", id[i],".pdf", sep = "")
ggsave(FileName, p,width = 6,height =hit/5)
FileName <- paste(netpath,"/ranImportant_Model_order", id[i], ".csv", sep = "")
write.csv(data,FileName)
nGenes = ncol(otu)
nSamples = nrow(otu)
moduleTraitCor = cor(MEs, envRDA[sample_names(ps.1),], use = "p")
moduleTraitPvalue = WGCNA::corPvalueStudent(moduleTraitCor, nSamples)
#sizeGrWindow(10,6)
# dim(MEs)[2]/2
# dim(envRDA)[2]/2
pdf(file=paste(netpath,"/","Module-env_relationships.pdf",sep = ""),width=dim(envRDA)[2]/2,height=dim(MEs)[2]/2)
# Will display correlations and their p-values
textMatrix = paste(signif(moduleTraitCor, 2), "\n(",
signif(moduleTraitPvalue, 1), ")", sep = "")
dim(textMatrix) = dim(moduleTraitCor)
par(mar = c(6, 8.5, 3, 3))
# Display the correlation values within a heatmap plot
WGCNA::labeledHeatmap(Matrix = moduleTraitCor,
xLabels = names(envRDA),
yLabels = names(MEs),
ySymbols = names(MEs),
colorLabels = FALSE,
colors = WGCNA::greenWhiteRed(50),
textMatrix = textMatrix,
setStdMargins = FALSE,
cex.text = 0.5,
zlim = c(-1,1),
main = paste("Module-trait relationships"))
dev.off()
#关键微生物与理化关系#--
# NetmodelEnv = paste(res1path,"/HUB_micro_env/",sep = "")
# dir.create(NetmodelEnv)
igraph = make_igraph(cor)
tem = 10
hub = hub_score(igraph)$vector %>%
sort(decreasing = TRUE) %>%
head(tem) %>%
as.data.frame()
colnames(hub) = "hub_sca"
p = ggplot(hub) +
geom_bar(aes(x = hub_sca,y = reorder(row.names(hub),hub_sca)),stat = "identity",fill = "#4DAF4A")
p
FileName <- paste(netpath,"/hub_micro", ".pdf", sep = "")
ggsave(FileName, p,width = 6,height =tem/2)
FileName <- paste(netpath,"/hub_micro", ".csv", sep = "")
write.csv(hub,FileName)
id.2 = row.names(hub)
# ps.1 = ps %>% scale_micro("TMM") %>%
otu = phyloseq::otu_table(ps.1)
tax = phyloseq::tax_table(ps.1)
head(otu)
data = otu[id.2,] %>% t() %>%
as.data.frame()
result = cor_env_ggcorplot(
env1 = envRDA[sample_names(ps.1),],
env2 = data,
label = F,
col_cluster = F,
row_cluster = F,
method = "spearman",
r.threshold= 0.5,
p.threshold= 0
)
p1 <- result[[1]]
p1
p2 <- result[[2]]
p2
hei = dim(env)[2]/5
#
# filename = paste(NetmodelEnv,"hum_env.csv",sep = "")
# write.csv(top10,filename)
filename = paste(netpath,"hum_env.pdf",sep = "")
ggsave(filename,p1,width = tem/2,height = dim(env)[2]/5)
filename = paste(netpath,"hum_env.pdf",sep = "")
ggsave(filename,p2,width = tem/2,height = dim(env)[2]/5)
filename = paste(netpath,"hum_env.jpg",sep = "")
ggsave(filename,p1,width = tem/2,height = dim(env)[2]/5)
filename = paste(netpath,"hum_env.jpg",sep = "")
ggsave(filename,p2,width = tem/2,height = dim(env)[2]/5)
}
Network analysis of bacterial and environmental factors（细菌和环境因子网络分析）
Envnetplot<- paste(res1path,"/Env_network",sep = "")
dir.create(Envnetplot)
ps16s = readRDS("./data/dataNEW/ps_16s.rds")%>% ggClusterNet::scale_micro()
psITS = NULL
library(phyloseq)
#--细菌和真菌ps对象中的map文件要一样
ps.merge <- ggClusterNet::merge16S_ITS(ps16s = ps16s,
psITS= psITS,
NITS = 200,
N16s = 200)
map = phyloseq::sample_data(ps.merge)
# head(map)
map$Group = "one"
phyloseq::sample_data(ps.merge) <- map
#--环境因子导入
data1 = env
envRDA.s = vegan::decostand(envRDA,"hellinger")
data1[,-1] = envRDA.s
Gru = data.frame(ID = colnames(env)[-1],group = "env" )
head(Gru)
library(sna)
library(ggClusterNet)
library(igraph)
result <- ggClusterNet::corBionetwork(ps = ps.merge,
N = 0,
r.threshold = 0.6, # 相关阈值
p.threshold = 0.05,
big = TRUE,
group = "Group",
env = data1, # 环境指标表格
envGroup = Gru,# 环境因子分组文件表格
layout_net = "model_maptree2",
path = Envnetplot,# 结果文件存储路径
fill = "Phylum", # 出图点填充颜色用什么值
size = "igraph.degree", # 出图点大小用什么数据
scale = TRUE, # 是否要进行相对丰度标准化
bio = TRUE, # 是否做二分网络
zipi = FALSE, # 是否计算ZIPI
step = 100, # 随机网络抽样的次数
width = 18,
label = TRUE,
height = 10
)
#> [1] "one"
#> num [1:34, 1:18] 0.0474 0.0633 0.0188 0.0588 0.069 ...
#> - attr(*, "dimnames")=List of 2
#> ..$ : chr [1:34] "pH" "SOC" "TN" "NH4.N" ...
#> ..$ : chr [1:18] "sample1" "sample10" "sample11" "sample12" ...
#> [1] "1"
#> [1] "2"
#> [1] "3"
p = result[[1]]
p
# 全部样本网络参数比对
data = result[[2]]
plotname1 = paste(Envnetplot,"/network_all.jpg",sep = "")
ggsave(plotname1, p,width = 15,height = 12,dpi = 72)
plotname1 = paste(Envnetplot,"/network_all.png",sep = "")
ggsave(plotname1, p,width = 10,height = 8,dpi = 72)
plotname1 = paste(Envnetplot,"/network_all.pdf",sep = "")
ggsave(plotname1, p,width = 15,height = 12)
tablename <- paste(Envnetplot,"/co-occurrence_Grobel_net",".csv",sep = "")
write.csv(data,tablename)Correlation analysis of network attributes and modules with environmental factors（网络属性-模块与环境因子相关分析）
net.property_env = function(
ps = ps,
corpath = corpath,
Top = 500,
r.threshold= 0.8,
p.threshold=0.05,
env = env,
select.mod = c("model_1","model_2","model_3"),
select.env = "pH"
){
map = sample_data(ps)
id3 = map$Group %>% unique()
for (m in 1:length(id3)) {
#---全部模块的微生物网络#——------
pst = ps %>%
filter_taxa(function(x) sum(x ) > 0, TRUE) %>%
scale_micro("rela") %>%
subset_samples.wt("Group",id3[m]) %>%
filter_OTU_ps(Top)
result = cor_Big_micro(ps = pst,
N = 0,
r.threshold= r.threshold,
p.threshold= p.threshold,
method = "spearman")
cor = result[[1]]
head(cor)
# igraph = make_igraph(cor)
#--计算模块信息，部分OTU没有模块，注意去除
# netClu = modulGroup( cor = cor,cut = NULL,method = "cluster_fast_greedy" )
# head(netClu)
# result2 = model_maptree_group(cor = cor,
# nodeGroup = netClu,
# )
result2 = model_maptree2(cor = cor, method = "cluster_fast_greedy")
#----多功能性和环境因子等和模块相关#-----
# 第一种是模块特征向量
#--基于模块的OTU，计算在不同分组中的总丰度zscore 并统计检验#-------
select.mod = select.mod
mod1 = result2[[2]]
head(mod1)
tem = mod1$group %>% table() %>%
as.data.frame() %>%
dplyr::arrange(desc(Freq))
colnames(tem) = c("Model","OTU.num")
head(tem)
if (length(select.mod) == 1 & is.numeric(select.mod)) {
select.mod.name = tem$Model[1:select.mod]
mod1 = mod1 %>% filter(!group == "mother_no",
group %in%c(select.mod.name)
) %>% select(ID,group,degree)
} else if (is.character(select.mod)) {
select.mod.name = select.mod
mod1 = mod1 %>% filter(!group == "mother_no",
group %in%c(select.mod.name)
) %>% select(ID,group,degree)
}
id.s = mod1$group %>% unique()
for (i in 1:length(id.s)) {
id.t = mod1 %>%
dplyr::filter(group %in% id.s[i]) %>%
.$ID
ps.t = ps %>%
scale_micro() %>%
subset_taxa.wt("OTU", id.t )
otu = ps.t %>%
vegan_otu() %>%
t()
colSD = function(x){
apply(x,2, sd)
}
dat = (otu - colMeans(otu))/colSD(otu)
head(dat)
otu_table(ps.t) = otu_table(as.matrix(dat),taxa_are_rows = TRUE)
#--计算总丰度
otu = ps.t %>% vegan_otu() %>% t()
colSums(otu)
dat = data.frame(id = names(colSums(otu)),abundance.zscore = colSums(otu))
colnames(dat)[2] = id.s[i]
if (i ==1) {
tem = dat
} else{
dat$id = NULL
tem = cbind(tem,dat)
}
}
head(tem)
map =sample_data(ps.t)
map$id = row.names(map)
map = map[,c("id","Group")]
data = map %>%
as.tibble() %>%
inner_join(tem,by = "id") %>%
dplyr::rename(group = Group)
colnames(env)[1] = "id"
# env1$id = row.names(env1)
subenv = env %>% dplyr::select(id,everything()) %>% select(id,select.env )
head(data)
tab = data %>% left_join(subenv,by = "id")
head(tab)
library(reshape2)
mtcars2 = melt(tab, id.vars=c(select.env,"group","id"))
mtcars2$variable
head(mtcars2)
lab = mean(mtcars2[,select.env])
p1_1 = ggplot2::ggplot(mtcars2,aes(x= value,!!sym(select.env), colour=variable)) +
ggplot2::geom_point() +
ggpubr::stat_cor(label.y=lab*1.1)+
ggpubr::stat_regline_equation(label.y=lab*1.1,vjust = 2) +
facet_wrap(~variable, scales="free_x") +
geom_smooth(aes(value,!!sym(select.env), colour=variable), method=lm, se=T)+
theme_classic()
p1_1
filename = paste(corpath,"/cor_netowrkmodel",select.env,".pdf",sep = "")
ggsave(filename,p1_1,width = 16,height = 18)
#-多功能性和网络属性相关#---------
igraph = make_igraph(cor)
dat = igraph::V(igraph)
names(dat) %>% length()
#--弄清楚每个样本包含的OTU数量
# pst = ps %>%
# scale_micro("rela") %>%
# phyloseq::subset_samples(Group %in% c("KO","WT","OE")) %>%
# filter_OTU_ps(500)
print("1")
# otu = pst %>%
# # phyloseq::subset_samples(Group %in% c("KO","WT","OE")) %>%
# # filter_OTU_ps(500) %>%
# subset_taxa(row.names(tax_table(pst)) %in% names(dat)) %>%
# vegan_otu() %>%
# t()
# print("1")
otu = pst %>% vegan_otu() %>% t()
otu = otu[row.names(otu) %in% names(dat),]
otu[otu > 1] = 1
dim(otu)
A = list()
dat.f = NULL
for (i in 1:length(colnames(otu))) {
tem = otu[,colnames(otu)[i]][otu[,colnames(otu)[i]] > 0 ] %>% names()
A[[colnames(otu)[i]]] = tem
#-计算性质
tem.2 = A[[colnames(otu)[i]]]
tem.g = igraph::induced_subgraph(igraph,tem.2)
dat = net_properties.2(tem.g,n.hub = FALSE)
head(dat,n = 16)
dat[16,1] = 0
dat = as.data.frame(dat)
dat$value = as.numeric(dat$value)
colnames(dat) = colnames(otu)[i]
if (i == 1) {
dat.f = dat
} else {
dat.f = cbind(dat.f,dat)
}
}
head(dat.f)
dat.f = dat.f %>%
t() %>%
as.data.frame()
select.env = select.env
# env1$id = row.names(env1)
# env1 = env1 %>% dplyr::select(id,everything()) %>% select(id,select.env )
head(dat.f)
dat.f$id = row.names(dat.f)
dat.f = dat.f %>% dplyr:: select(id,everything())
tab = dat.f %>% left_join(subenv,by = "id")
head(tab)
mtcars2 = melt(tab, id.vars=c(select.env,"id"))
lab = mean(mtcars2[,select.env])
head(mtcars2)
p0_1 = ggplot2::ggplot(mtcars2,aes(x= value,!!sym(select.env), colour=variable)) +
ggplot2::geom_point() +
ggpubr::stat_cor(label.y=lab*1.1)+
ggpubr::stat_regline_equation(label.y=lab*1.1,vjust = 2) +
facet_wrap(~variable, scales="free_x") +
geom_smooth(aes(value,!!sym(select.env), colour=variable), method=lm, se=T)+
theme_classic()
p0_1
filename = paste(corpath,"/cor_netowrkpropertities",select.env,".pdf",sep = "")
ggsave(filename,p0_1,width = 16,height = 18)
}
}
library(tidyverse)
library(igraph)
corpath = paste(res1path,"/env_difference_plot/",sep = "")
dir.create(corpath)
net.property_env(
ps = ps,
corpath = corpath,
Top = 500,
r.threshold= 0.8,
p.threshold=0.05,
env = env,
select.mod = c("model_1","model_2","model_3"),
select.env = "pH")
#> [1] "1"
#> [1] "1"
#> [1] "1"Co-occurrence network of bacterial-fungal-environmental factors（细菌-真菌-环境因子三者共现网络）
Envnetplot<- paste(res1path,"/16s_ITS_Env_network",sep = "")
dir.create(Envnetplot)
ps16s = readRDS("./data/dataNEW//ps_16s.rds") %>% ggClusterNet::scale_micro()
psITS = readRDS("./data/dataNEW//ps_ITS.rds")%>% ggClusterNet::scale_micro()
library(phyloseq)
#--细菌和真菌ps对象中的map文件要一样
ps.merge <- ggClusterNet::merge16S_ITS(ps16s = ps16s,
psITS = psITS,
NITS = 200,
N16s = 200)
ps.merge
#> phyloseq-class experiment-level object
#> otu_table() OTU Table: [ 400 taxa and 18 samples ]
#> sample_data() Sample Data: [ 18 samples by 2 sample variables ]
#> tax_table() Taxonomy Table: [ 400 taxa by 8 taxonomic ranks ]
map = phyloseq::sample_data(ps.merge)
# head(map)
map$Group = "one"
phyloseq::sample_data(ps.merge) <- map
data1 = env
envRDA.s = vegan::decostand(envRDA,"hellinger")
data1[,-1] = envRDA.s
Gru = data.frame(ID = colnames(env)[-1],group = "env" )
head(Gru)
library(sna)
library(ggClusterNet)
library(igraph)
result <- ggClusterNet::corBionetwork(ps = ps.merge,
N = 0,
r.threshold = 0.6, # 相关阈值
p.threshold = 0.05,
big = TRUE,
group = "Group",
env = data1, # 环境指标表格
envGroup = Gru,# 环境因子分组文件表格
layout_net = "model_maptree2",
path = Envnetplot,# 结果文件存储路径
fill = "Phylum", # 出图点填充颜色用什么值
size = "igraph.degree", # 出图点大小用什么数据
scale = TRUE, # 是否要进行相对丰度标准化
bio = TRUE, # 是否做二分网络
zipi = FALSE, # 是否计算ZIPI
step = 100, # 随机网络抽样的次数
width = 18,
label = TRUE,
height = 10
)
#> [1] "one"
#> num [1:34, 1:18] 0.0474 0.0633 0.0188 0.0588 0.069 ...
#> - attr(*, "dimnames")=List of 2
#> ..$ : chr [1:34] "pH" "SOC" "TN" "NH4.N" ...
#> ..$ : chr [1:18] "sample1" "sample10" "sample11" "sample12" ...
#> [1] "1"
#> [1] "2"
#> [1] "3"
p = result[[1]]
p
# 全部样本网络参数比对
data = result[[2]]
plotname1 = paste(Envnetplot,"/network_all.jpg",sep = "")
ggsave(plotname1, p,width = 15,height = 12,dpi = 72)
plotname1 = paste(Envnetplot,"/network_all.png",sep = "")
ggsave(plotname1, p,width = 10,height = 8,dpi = 72)
plotname1 = paste(Envnetplot,"/network_all.pdf",sep = "")
ggsave(plotname1, p,width = 15,height = 12)
tablename <- paste(Envnetplot,"/co-occurrence_Grobel_net",".csv",sep = "")
write.csv(data,tablename)Bacterial-fungal cross-domain networks(OTU network - domain network - bipartite network )（细菌和真菌跨域网络分析-OTU网络-域网络-二分网络）
# 仅仅关注细菌和真菌之间的相关，不关注细菌内部和真菌内部相关
Envnetplot<- paste(res1path,"/16S_ITS_network",sep = "")
dir.create(Envnetplot)
ps16s = readRDS("./data/dataNEW/ps_16s.rds")
psITS = readRDS("./data/dataNEW/ps_ITS.rds")
#--细菌和真菌ps对象中的map文件要一样
ps.merge <- ggClusterNet::merge16S_ITS(ps16s = ps16s,
psITS = psITS,
N16s = 300,
NITS = 300
)
ps.merge
#> phyloseq-class experiment-level object
#> otu_table() OTU Table: [ 600 taxa and 18 samples ]
#> sample_data() Sample Data: [ 18 samples by 2 sample variables ]
#> tax_table() Taxonomy Table: [ 600 taxa by 8 taxonomic ranks ]
map = phyloseq::sample_data(ps.merge)
# head(map)
map$Group = "one"
phyloseq::sample_data(ps.merge) <- map
data = NULL
library(sna)
library(igraph)
library(ggClusterNet)
library(phyloseq)
result <- corBionetwork(ps = ps.merge,
N = 0,
lab = data,
r.threshold = 0.6, # 相关阈值
p.threshold = 0.05,
group = "Group",
# env = data1, # 环境指标表格
# envGroup = Gru,# 环境因子分组文件表格
layout_net = "model_maptree2",
path = Envnetplot,# 结果文件存储路径
fill = "Phylum", # 出图点填充颜色用什么值
size = "igraph.degree", # 出图点大小用什么数据
scale = TRUE, # 是否要进行相对丰度标准化
bio = TRUE, # 是否做二分网络
zipi = F, # 是否计算ZIPI
step = 100, # 随机网络抽样的次数
width = 12,
label = TRUE,
height = 10,
big = TRUE,
select_layout = TRUE,
# layout_net = "model_maptree",
clu_method = "cluster_fast_greedy"
)
#> [1] "one"
#> [1] "1"
#> [1] "2"
#> [1] "3"
tem <- model_maptree(cor =result[[5]],
method = "cluster_fast_greedy",
seed = 12
)
node_model = tem[[2]]
head(node_model)
p = result[[1]]
p
# 全部样本网络参数比对
data = result[[2]]
plotname1 = paste(Envnetplot,"/network_all.pdf",sep = "")
ggsave(plotname1, p,width = 10,height = 8)
tablename <- paste(Envnetplot,"/co-occurrence_Grobel_net",".csv",sep = "")
write.csv(data,tablename)
tablename <- paste(Envnetplot,"/node_model_imformation",".csv",sep = "")
write.csv(node_model,tablename)
tablename <- paste(Envnetplot,"/nodeG_plot",".csv",sep = "")
write.csv(result[[4]],tablename)
tablename <- paste(Envnetplot,"/edge_plot",".csv",sep = "")
write.csv(result[[3]],tablename)
tablename <- paste(Envnetplot,"/cor_matrix",".csv",sep = "")
write.csv(result[[5]],tablename)Bipartite network of bacteria and fungi at any taxonomic level（细菌真菌的任意水平二分网络）
library(tidyverse)
# res1path = "result_and_plot/Micro_and_other_index_16s/"
Envnetplot<- paste(res1path,"/16S_ITS_network_Genus",sep = "")
dir.create(Envnetplot)
ps16s = readRDS("./data/dataNEW/ps_16s.rds")
psITS = readRDS("./data/dataNEW/ps_ITS.rds")
#--细菌和真菌ps对象中的map文件要一样
ps.merge <- merge16S_ITS(ps16s = ps16s,
psITS = psITS,
N16s = 300,
NITS = 300
)
ps.merge
#> phyloseq-class experiment-level object
#> otu_table() OTU Table: [ 600 taxa and 18 samples ]
#> sample_data() Sample Data: [ 18 samples by 2 sample variables ]
#> tax_table() Taxonomy Table: [ 600 taxa by 8 taxonomic ranks ]
map = phyloseq::sample_data(ps.merge)
# head(map)
map$Group = "one"
phyloseq::sample_data(ps.merge) <- map
tem.0 = ps.merge %>% tax_glom_wt(ranks = "Genus")
tax = tem.0 %>% vegan_tax() %>%
as.data.frame()
head(tax)
data = NULL
library(sna)
library(igraph)
library(ggClusterNet)
library(phyloseq)
library(WGCNA)
result <- corBionetwork(ps = tem.0,
N = 0,
lab = data,
r.threshold = 0.6, # 相关阈值
p.threshold = 0.05,
group = "Group",
# env = data1, # 环境指标表格
# envGroup = Gru,# 环境因子分组文件表格
# layout = "fruchtermanreingold",
path = Envnetplot,# 结果文件存储路径
fill = "Phylum", # 出图点填充颜色用什么值
size = "igraph.degree", # 出图点大小用什么数据
scale = TRUE, # 是否要进行相对丰度标准化
bio = TRUE, # 是否做二分网络
zipi = F, # 是否计算ZIPI
step = 100, # 随机网络抽样的次数
width = 12,
label = TRUE,
height = 10,
big = TRUE,
select_layout = TRUE,
layout_net = "model_maptree2",
clu_method = "cluster_fast_greedy"
)
#> [1] "one"
#> [1] "1"
#> [1] "2"
#> [1] "3"
tem <- model_maptree(cor =result[[5]],
method = "cluster_fast_greedy",
seed = 12
)
node_model = tem[[2]]
head(node_model)
otu = tem.0 %>% vegan_otu() %>%
as.data.frame()
node_model = node_model[match(colnames(otu),node_model$ID),]
MEList = moduleEigengenes(otu, colors = node_model$group)
MEs = MEList$eigengenes
nGenes = ncol(otu)
nSamples = nrow(otu)
moduleTraitCor = cor(MEs, envRDA, use = "p")
moduleTraitPvalue = corPvalueStudent(moduleTraitCor, nSamples)
#sizeGrWindow(10,6)
pdf(file=paste(Envnetplot,"/","Module-env_relationships.pdf",sep = ""),width=10,height=6)
# Will display correlations and their p-values
textMatrix = paste(signif(moduleTraitCor, 2), "\n(",
signif(moduleTraitPvalue, 1), ")", sep = "")
dim(textMatrix) = dim(moduleTraitCor)
par(mar = c(6, 8.5, 3, 3))
# Display the correlation values within a heatmap plot
labeledHeatmap(Matrix = moduleTraitCor,
xLabels = names(envRDA),
yLabels = names(MEs),
ySymbols = names(MEs),
colorLabels = FALSE,
colors = greenWhiteRed(50),
textMatrix = textMatrix,
setStdMargins = FALSE,
cex.text = 0.5,
zlim = c(-1,1),
main = paste("Module-trait relationships"))
dev.off()
#> png
#> 2
p = result[[1]]
p
# 全部样本网络参数比对
data = result[[2]]
plotname1 = paste(Envnetplot,"/network_all.pdf",sep = "")
ggsave(plotname1, p,width = 10,height = 8)
tablename <- paste(Envnetplot,"/co-occurrence_Grobel_net",".csv",sep = "")
write.csv(data,tablename)
tablename <- paste(Envnetplot,"/nodeG_plot",".csv",sep = "")
write.csv(result[[4]],tablename)
tablename <- paste(Envnetplot,"/edge_plot",".csv",sep = "")
write.csv(result[[3]],tablename)
tablename <- paste(Envnetplot,"/cor_matrix",".csv",sep = "")
write.csv(result[[5]],tablename)Phylogenetic analysis- data preparation.（系统发育分析-准备数据）
library(picante)
library(ape)
library(vegan)
library(FSA)
library(eulerr)
library(grid)
library(gridExtra)
require(minpack.lm)
require(Hmisc)
require(stats4)
library(parallel)
env = read.csv("./data/dataNEW/env.csv")
envRDA = env
head(env)row.names(envRDA) = env$ID
envRDA$ID = NULL
head(envRDA)
psphy = filter_taxa(ps, function(x) sum(x ) > 1000 , TRUE);psphy
#> phyloseq-class experiment-level object
#> otu_table() OTU Table: [ 597 taxa and 18 samples ]
#> sample_data() Sample Data: [ 18 samples by 2 sample variables ]
#> tax_table() Taxonomy Table: [ 597 taxa by 7 taxonomic ranks ]
#> phy_tree() Phylogenetic Tree: [ 597 tips and 595 internal nodes ]
#> refseq() DNAStringSet: [ 597 reference sequences ]
res1path = "./result_and_plot/"
phypath = paste(res1path,"/Phylogenetic_analyse_spacies/",sep = "")
dir.create(phypath)
map = sample_data(ps)
n = map$Group %>% unique() %>%
length()
n
#> [1] 3Neutral model（中性模型）
neutralModel = function(otu = NULL,
tax = NULL,
map = NULL,
tree = NULL,
ps = NULL,
group = "Group",
ncol = 3,
nrow = 1
){
# 抽平，默认使用最小序列抽平
ps = inputMicro(otu,tax,map,tree,ps,group = group)
ps
set.seed(72) #设置随机种子，保证结果可重复
psrare = rarefy_even_depth(ps)
# 标准化
ps.norm = transform_sample_counts(psrare, function(x) x/sum(x))
#------------------------------------------开始计算中性模型----------------------------------------------------------
map = as.data.frame(sample_data(psrare))
aa = levels(map$Group)
aa
map$ID = row.names(map)
plots = list()
dat1 = list()
dat2 = list()
i =1
for (i in 1:length(aa)) {
maps<- dplyr::filter(as.tibble(map),Group %in%aa[i])
maps = as.data.frame(maps)
row.names(maps) = maps$ID
ps_sub = psrare
sample_data(ps_sub) =maps ;ps_sub
# 提取OTU表格
OTU.table = t(otu_table(ps_sub))
head(OTU.table )
# 将整个群落看做一个整体，计算每个样本的序列数，并求取均值Calculate the number of individuals in the meta community (Average read depth)
N <- mean(apply(OTU.table, 1, sum))
#计算每个OTU的的平均序列数 Calculate the average relative abundance of each taxa across communities
p.m <- apply(OTU.table, 2, mean)
#去除OTU序列数为0的OTU
p.m <- p.m[p.m != 0]
p <- p.m/N
p.df = data.frame(p) %>%
rownames_to_column(var="OTU")
# Calculate the occurrence frequency of each taxa
OTU.table.bi <- 1*(OTU.table>0)
freq.table <- apply(OTU.table.bi, 2, mean)
freq.table <- freq.table[freq.table != 0]
freq.df = data.frame(OTU=names(freq.table), freq=freq.table)
#Combine
C <- inner_join(p.df,freq.df, by="OTU") %>%
arrange(p)
# Remove rows with any zero (absent in either source pool or local communities). You already did this, but just to make sure we will do it again.
C.no0 <- C %>%
filter(freq != 0, p != 0)
#Calculate the limit of detection
d <- 1/N
##Fit model parameter m (or Nm) using Non-linear least squares (NLS)
p.list <- C.no0$p
freq.list <- C.no0$freq
m.fit <- nlsLM(freq.list ~ pbeta(d, N*m*p.list, N*m*(1-p.list), lower.tail=FALSE), start=list(m=0.1))
m.ci <- confint(m.fit, 'm', level=0.95)
m.sum <- summary(m.fit)
m.coef = coef(m.fit)
freq.pred <- pbeta(d, N*coef(m.fit)*p.list, N*coef(m.fit)*(1-p.list), lower.tail=FALSE)
Rsqr <- 1 - (sum((freq.list - freq.pred)^2))/(sum((freq.list - mean(freq.list))^2))
# Get table of model fit stats
fitstats <- data.frame(m=m.coef, m.low.ci=m.ci[1], m.up.ci=m.ci[2],
Rsqr=Rsqr, p.value=m.sum$parameters[4], N=N,
Samples=nrow(OTU.table), Richness=length(p.list),
Detect=d)
# Get confidence interval for predictions
freq.pred.ci <- binconf(freq.pred*nrow(OTU.table), nrow(OTU.table), alpha=0.05, method="wilson", return.df=TRUE)
# Get table of predictions
pred.df <- data.frame(metacomm_RA=p.list, frequency=freq.pred,
frequency_lowerCI=freq.pred.ci[,2],
frequency_upperCI=freq.pred.ci[,3]) %>%
unique()
# Get table of observed occupancy and abundance
obs.df = C.no0 %>%
dplyr::rename(metacomm_RA = p, frequency=freq)
head(obs.df)
p = ggplot(data=obs.df) +
geom_point(data=obs.df, aes(x=log10(metacomm_RA), y=frequency),
alpha=.3, size=2, color="#8DD3C7") +
geom_line(data=pred.df, aes(x=log10(metacomm_RA), y=frequency), color="#FFFFB3") +
geom_line(data=pred.df, aes(x=log10(metacomm_RA), y=frequency_lowerCI), linetype=2, color="#FFFFB3") +
geom_line(data=pred.df, aes(x=log10(metacomm_RA), y=frequency_upperCI), linetype=2, color="#FFFFB3") +
# geom_text(data=fitstats, aes(label = paste("R^2 == ", round(Rsqr, 3))),
# x=1, y=0.75, size=4, parse=TRUE) +
# geom_text(data=fitstats, aes(label = paste("italic(m) ==", round(m, 3))),
# x=-1, y=0.85, size=4, parse=TRUE) +
labs(x="Log10 abundance in\nmetacommunity", y="Frequency detected",title = paste(aa[i],paste("R^2 == ", round(fitstats$Rsqr, 3)),paste("italic(m) ==", round(fitstats$m, 3)))) +
theme_bw() +
theme(axis.line = element_line(color="black"),
legend.position = "none",
axis.title = element_text(size=14),
axis.text = element_text(size=12))
p
plots[[aa[i]]] = p
dat1[[aa[i]]] = obs.df
dat2[[aa[i]]] = pred.df
}
# plots$ABCD
# library(ggpubr)
# nrow=2,,ncol=4
p = ggpubr::ggarrange(plotlist = plots,common.legend = TRUE, legend="right",ncol = ncol,nrow = nrow)
p
return(list(p,plots,dat1,dat2))
}
result = neutralModel(ps = ps,group = "Group",ncol = 3)
#--合并图表
p1 = result[[1]]
p1
FileName <- paste(phypath,"1_neutral_modelCul", ".pdf", sep = "")
ggsave(FileName, p1,width = 12,height = 4)
FileName <- paste(phypath,"1_neutral_modelCul", ".png", sep = "")
ggsave(FileName, p1,width = 12,height = 4)
#--系统发育信号
phyloSignal = function(otu = NULL,
tax = NULL,
map = NULL,
tree = NULL ,
ps = NULL,
env = env,
group = "Group",
path = "./"){
# 抽平，默认使用最小序列抽平
ps = inputMicro(otu,tax,map,tree,ps,group = group)
ps
# 抽平
set.seed(72)
psrare = rarefy_even_depth(ps)
# 标准化
ps.norm = transform_sample_counts(psrare, function(x) x/sum(x))
map = as.data.frame(sample_data(psrare))
mapE =merge(map,env,by = "row.names",all= TRUE)
row.names(mapE) = mapE$Row.names
mapE$Row.names = NULL
mapE$ID = row.names(mapE)
sample_data(ps.norm) = mapE
aa = levels(mapE$Group)
dir.create(path)
#----------分组计算门特尔相关,将结果保存，因为计算时间很长，只需计算一个就好了#-------
eco = "Endosp."
for (eco in as.character(unique(mapE$Group))){
# Subset data
print(paste("Now running", eco))
# sub.physeq = phyloseq::subset_samples(ps.norm , Group == eco)
sub.physeq = ps.norm
otu = as.data.frame(vegan_otu(ps.norm))
head(otu)
map = as.data.frame(sample_data(ps.norm))
mapsub <- map[map$Group == eco,]
sample_data(sub.physeq) = mapsub
# Remove OTUs not found in at least 3 samples
OTU.table = otu_table(sub.physeq)
OTU.table[OTU.table > 0] = 1
OTU.freq = rowSums(OTU.table)
OTU.freq = OTU.freq[OTU.freq > 2]
sub.physeq = prune_taxa(names(OTU.freq), sub.physeq)
sub.physeq
# get phylogenetic distances
tree = phy_tree(sub.physeq)
phylo.dist = cophenetic(tree)
sample_OTUs = tree$tip.label
sam.phylo.dist = phylo.dist[sample_OTUs, sample_OTUs]
sam.phylo.dist[upper.tri(sam.phylo.dist, diag=TRUE)] = NA
# Generate dataframe of niche preference for pH, SOC and CN
# site.chem.mat = data.frame(sample_data(sub.physeq)) %>%
# # mutate(CN = percent_C / percent_N) %>%
# dplyr::select(ID, colnames(env))
site.chem.mat = env[row.names(env) %in% row.names(mapsub),]
# rownames(site.chem.mat) = site.chem.mat$ID
# site.chem.mat$ID = NULL
site.chem.mat = as.matrix(site.chem.mat)
otu.table = t(otu_table(sub.physeq))
# head(otu.table)
match(row.names(otu.table),row.names(site.chem.mat))
OTU.niche = wascores(site.chem.mat, otu.table)
OTU.niche.df = data.frame(OTU.niche)
head( OTU.niche.df)
# i =1
for (i in 1:dim(OTU.niche.df)[2]) {
pH.pref = OTU.niche.df[[i]]
names(pH.pref) = rownames(OTU.niche.df)
pH.dist = as.matrix(dist(pH.pref), labels=TRUE)
sam.pH.dist = pH.dist[sample_OTUs, sample_OTUs]
sam.pH.dist[upper.tri(sam.pH.dist, diag=TRUE)] = NA
sam.pH.crlg = mantel.correlog(sam.pH.dist, sam.phylo.dist)
# ?mantel.correlog
filename = paste(path,eco,colnames(OTU.niche.df[i]), "_crlg.rds", sep="_")
saveRDS(sam.pH.crlg, file=filename)
}
}
}
phySigPlot = function(otu = NULL,
tax = NULL,
map = NULL,
tree = NULL,
ps = NULL,
group = "Group",
env = env,
path = "./"){
# 抽平，默认使用最小序列抽平
ps = inputMicro(otu,tax,map,tree,ps,group = group)
ps
mapE = as.data.frame(sample_data(ps))
for (eco in levels(mapE$Group)) {
# eco = "KO"
# i = 1
for (i in 1:length(colnames(env))) {
ag.pH.crlg = data.frame(readRDS(file=paste(path,eco,colnames(env[i]), "_crlg.rds", sep="_"))$mantel.res) %>%
mutate(Group = eco, property = colnames(env)[i])
if (i == 1) {
data = ag.pH.crlg
}
if (i != 1) {
data = rbind(data,ag.pH.crlg )
}
}
if (eco == levels(mapE$Group)[1]) {
data2 = data
}
if (eco != levels(mapE$Group)[1]) {
data2 = rbind(data2, data)
}
}
dim(data2)
eco.crlg = data2 %>%
mutate(sig = ifelse(Pr.corrected. <= 0.05, "significant", "non-significant")) %>%
filter(!(is.na(Pr.corrected.)))
eco.crlg$Group= factor(eco.crlg$Group)
p = ggplot(data=eco.crlg, aes(x=class.index, y=Mantel.cor)) +
geom_point(data=eco.crlg[eco.crlg$sig=="significant",], color = "black", size=2, shape=16) +
geom_point(data=eco.crlg[eco.crlg$sig=="non-significant",], color = "black",size=2, shape=1) +
geom_line(data=eco.crlg, aes(color=property)) +
geom_hline(yintercept = 0, linetype=2) +
labs(x = "Phylogenetic distance class", y="Mantel correlation", color="property") +
# facet_grid(~Group)
facet_wrap(~Group,scales="free_y",ncol = 4)
return(list(p,eco.crlg,data2))
}
env = read.csv("./data/dataNEW/env.csv")
head(env)envRDA = env
head(env)row.names(envRDA) = env$ID
envRDA$ID = NULL
head(envRDA)
#
phypath2 = paste(phypath,"/phyloSignal/",sep = "")
dir.create(phypath)
phyloSignal(ps = ps %>% filter_OTU_ps(400),
group = "Group",
env = envRDA[,2:3],
path = phypath2)
#> [1] "Now running Group1"
#> [1] "Now running Group2"
#> [1] "Now running Group3"
result = phySigPlot(ps = ps,group = "Group",env = envRDA[,2:3],path = phypath2)
#
#提取图片
p2 = result[[1]] + mytheme1
p2
#-提取作图数据
data = result[[2]]
head(data)
FileName <- paste(phypath,"2_phySigPlot", ".pdf", sep = "")
ggsave(FileName, p2,width = 15,height = 6)
FileName <- paste(phypath,"2_phySigPlot", ".csv", sep = "")
write.csv(data,FileName)Null model calculation （计算零模型）
nullModel <- function(otu = NULL,
tax = NULL,
map = NULL,
tree = NULL ,
ps = NULL,
group = "Group",
dist.method = "bray",
gamma.method = "total",
transfer = "none",
null.model = "ecosphere"){
ps = inputMicro(otu,tax,map,tree,ps,group = group)
map = as.data.frame(sample_data(ps))
grp1 = unique(map$Group)
grp=list()
### 制作分组列表
for (i in 1:length(grp1)) {
grp[[i]]=rownames(map)[which(map$Group==grp1[i])]
}
names(grp) = grp1
report = c()
dat4anova = c()
grp4anova = c()
report.ES = c()
report.SES = c()
# x=17
otu = as.data.frame(t(vegan_otu(ps)))
otu = as.matrix(otu)
for(x in c(1:length(grp))){ #
#print(paste("Group",x))
dataCK1 = otu[,grp[[x]]]
##delete empty rows
if(gamma.method == "group"){
rsum1 = rowSums(dataCK1)
tempCK1 = which(rsum1==0)
if(length(tempCK1)!=0) {dataCK1 = dataCK1[-tempCK1,]}
}
# 分组，对一组计算距离
beta.dist = vegdist(t(dataCK1),method = dist.method)
# 转化为相似性距离
similarity.ob = 1 - beta.dist
#similarity.ob.sd = sd(1-beta.dist, na.rm=TRUE)
# 统计有多少个OTU
gamma = nrow(dataCK1)
#统计每个样本的OTU数量
alpha = colSums(dataCK1>0)
# OTU求和
if(gamma.method == "group"){
occur = apply(dataCK1, MARGIN=1, FUN=sum)
}else{
occur = apply(otu, MARGIN=1, FUN=sum) #otu[valid.row,]
}
#print(paste(similarity.ob, similarity.ob.sd))
r = 100
# 构建样本矩阵，空矩阵
similarity.pm = matrix(0, nrow=ncol(dataCK1), ncol=ncol(dataCK1))
similarity.pm = as.dist(similarity.pm)
# i = 1
for(i in 1:r){
#print(i)
# 构造OTU矩阵孔阵
PRM1 = matrix(0, ncol= ncol(dataCK1), nrow = nrow(dataCK1))
if(null.model == "ecosphere"){
# j = 1
for(j in 1:ncol(dataCK1)){
# 提取该样本otu大于0的全部otu
aa = dataCK1[dataCK1[,j]>0,j]
PRM1[sample(1:gamma, alpha[j], replace=FALSE, prob=occur), j] = aa
}
}else if(null.model == "ecosim"){
PRM1 = randomizeMatrix(dataCK1, null.model="independentswap")
}else if(null.model == "frequency"){
PRM1 = randomizeMatrix(dataCK1, null.model="frequency")
}
# 计算抽的的矩阵的距离
dist_pm = vegdist(t(PRM1),method = dist.method)
# 将距离转化相似度放到之前构建的空阵中
similarity.pm = similarity.pm + (1- dist_pm)
}
similarity.pm = similarity.pm/r
#plot(density(similarity.pm[i,]))
normality = shapiro.test(similarity.pm)#正态性检测
nor.p = normality$p.value
ttest = t.test(similarity.pm, similarity.ob, alternative="two.sided", paired = TRUE, conf.level = 0.95)
tt.p = ttest$p.value
conf.int = ttest$conf.int
pm.mean = mean(similarity.pm)
pm.sd = sd(similarity.pm)
ES = log(similarity.ob) - log(similarity.pm)
effect.size = mean(ES)
effect.size.sd = sd(ES)
SES = (similarity.ob - similarity.pm)/pm.sd
sd.effect.size = mean(SES)
sd.effect.size.sd = sd(SES)
ratio = 1 - similarity.pm / similarity.ob
ratio.mean = mean(ratio)
ratio.sd = sd(ratio)
dat4anova = c(dat4anova, as.vector(ratio))
grp4anova = c(grp4anova, rep(names(grp)[x], length(ratio)))
conf.int.str = paste("[",paste(signif(conf.int,digits=3),collapse="~"),"]",sep="")
report = rbind(report, c(mean(similarity.ob),sd(similarity.ob), pm.mean, pm.sd, conf.int.str, nor.p, tt.p , effect.size, effect.size.sd, sd.effect.size, sd.effect.size.sd, ratio.mean, ratio.sd))
report.ES = c(report.ES, effect.size)
report.SES = c(report.SES, sd.effect.size)
}
rownames(report) = grp1
colnames(report) = c("Mean of observed similarity", "Standard deviation of observed similarity",
"Mean of permutated similarity", "Standard deviation of permutated similarity",
"95% Conf int of perm similarity", "Normality test (p) on Perm similarity",
"T test on Ob and Perm similarity", "Effect size (ES)", "SD of ES",
"Standardized effect size (SES)", "SD of SES", "Mean of Ratio", "SD of Ratio")
head(report)
rep = t(report)
head(rep)
# 这个统计量代表不同群落之间是否有差异
##将零模型的统计检验结果保存到文件中。
if (length(unique(grp4anova)) > 1) {
aov.re = aov(dat4anova ~ grp4anova)
} else {
aov.re = NULL
}
#---------------将比例保存起来备用
ratio = data.frame(ratio = dat4anova,group = grp4anova)
return(list(rep,ratio,aov.re))
}
result <- nullModel(ps = psphy,
group="Group",
dist.method = "bray",
gamma.method = "total",
transfer = "none",
null.model = "ecosphere"
)
#--分组零模型运行结果
nullModeltab <- result[[1]]
# 比例
ratiotab <- result[[2]]
#-统计量统计差异
aovtab <- result[[3]]
FileName <- paste(phypath,"3_nullModeltab", ".csv", sep = "")
write.csv(nullModeltab,FileName)
FileName <- paste(phypath,"3_ratiotab", ".csv", sep = "")
write.csv(ratiotab,FileName)βNTI-nearest taxon index（最近种间亲缘关系指数）
bNTICul = function(otu = NULL,tax = NULL,map = NULL,tree = NULL ,ps = NULL,group = "Group",num = 99,thread = 1){
ps = inputMicro(otu,tax,map,tree,ps,group = group)
ps
ps_sub <- ps
# tree = phy_tree(ps)
# tree
#-------------调整map文件-----------------------------------------------------------------
#添加一个ID列
map = as.data.frame(sample_data(ps_sub))
map$ID = row.names(map)
sample_data(ps) = map
#-----------准备OTU表格---------------------抽平-不设置抽平条数，默认按照最小序列数数目抽平
set.seed(72) # setting seed for reproducibility
psrare = rarefy_even_depth(ps_sub)
#检查序列数量
sample_sums(psrare)
# 标准化数据
ps.norm = transform_sample_counts(psrare, function(x) x/sum(x))
# 计算βMNTD对每个随机零模型群落
bMNTD_null_func <- function(i, OTU.table, tree){
tree$tip.label = sample(tree$tip.label)
bMNTD_s = comdistnt(OTU.table, cophenetic(tree), abundance.weighted = TRUE)
A <- attr(bMNTD_s, "Size")
B <- if (is.null(attr(bMNTD_s, "Labels"))) sequence(A) else attr(bMNTD_s, "Labels")
if (isTRUE(attr(bMNTD_s, "Diag"))) attr(bMNTD_s, "Diag") <- FALSE
if (isTRUE(attr(bMNTD_s, "Upper"))) attr(bMNTD_s, "Upper") <- FALSE
bMNTD_s.df = data.frame(Sample_1 = B[unlist(lapply(sequence(A)[-1], function(x) x:A))],
Sample_2 = rep(B[-length(B)], (length(B)-1):1),
bMNTD = as.vector(bMNTD_s),
rep=i)
return(bMNTD_s.df)
}
# 计算βNTI
Phylo_turnover <- function(physeq, reps, nproc){
# Extract OTU table
OTU.table = t(otu_table(physeq))
# Extract phylogenetic tree
tree = phy_tree(physeq)
# Get βMNTD between all communities
bMNTD_o = comdistnt(OTU.table, cophenetic(tree), abundance.weighted = TRUE)
A <- attr(bMNTD_o, "Size")
B <- if (is.null(attr(bMNTD_o, "Labels"))) sequence(A) else attr(bMNTD_o, "Labels")
if (isTRUE(attr(bMNTD_o, "Diag"))) attr(bMNTD_o, "Diag") <- FALSE
if (isTRUE(attr(bMNTD_o, "Upper"))) attr(bMNTD_o, "Upper") <- FALSE
bMNTD_o.df = data.frame(Sample_1 = B[unlist(lapply(sequence(A)[-1], function(x) x:A))],
Sample_2 = rep(B[-length(B)], (length(B)-1):1),
bMNTD = as.vector(bMNTD_o))
# Get βMNTD for randomized null communities
rep.list = seq(1, reps)
bMNTD_s.df.list = mclapply(rep.list, bMNTD_null_func, OTU.table=OTU.table, tree=tree, mc.cores=nproc)
# Combine all data together and calculate βNTI for each sample pair
bMNTD_s.df <- do.call("rbind", bMNTD_s.df.list)
bMNTD_s.means.df = bMNTD_s.df %>%
group_by(Sample_1, Sample_2) %>%
dplyr::summarize(mean_bMNTD = mean(bMNTD),
sd_bMNTD = sd(bMNTD))
bMNTD_o.df = inner_join(bMNTD_o.df, bMNTD_s.means.df, by=c("Sample_1", "Sample_2")) %>%
mutate(bNTI = (bMNTD - mean_bMNTD)/sd_bMNTD)
return(bMNTD_o.df)
}
#========这里一把单核就真实数据而言需要超过10个小时，跑999次，所以需要多核
# 计算bnti，这里可以设置线程数量，是第三个参数，我们在linux下面可以设置，30个线程
# 第二个参数设置迭代数量，这里文献一般999嘛。
bNTI = Phylo_turnover(psrare, num, thread)
return(list(bNTI))
}
result = bNTICul(ps = psphy,group = "Group",num = 100,thread = 1)
bNTI = result[[1]]
head(bNTI)
filename = paste(phypath,"/4_bNTI.csv",sep = "")
write.csv(bNTI, filename)RCbray calculation（计算RCbray）
RCbary = function(otu = NULL,tax = NULL,map = NULL,tree = NULL ,ps = NULL,group = "Group",num = 99,thread = 1){
ps_sub <- ps
#----------------整理map文件
map = as.data.frame(sample_data(ps_sub))
map$ID = row.names(map)
sample_data(ps) = map
#-------------------准备OTU表格
#-----------------抽平-不设置抽平条数，默认按照最小序列数数目抽平
set.seed(72) # setting seed for reproducibility
psrare = rarefy_even_depth(ps_sub )
#检查序列数量
sample_sums(psrare)
# 标准化数据
ps.norm = transform_sample_counts(psrare, function(x) x/sum(x))
#--------------两个函数
# 对模拟群落计算距离
RCbray_null_func <- function(i, freq.abd.df, alpha1, alpha2, N){
# Get simulated communities and distance
## initally select OTUs weighted by their frequency. The number of OTUs selected should equal the richness of the samples.
simcom1 = data.frame(table(sample(freq.abd.df$OTU, size=alpha1, replace=FALSE, prob=freq.abd.df$freq)), stringsAsFactors = F)
colnames(simcom1) = c("OTU","simcom1")
simcom1$OTU = as.character(simcom1$OTU)
simcom1 = inner_join(simcom1, freq.abd.df, by="OTU")
simcom2 = data.frame(table(sample(freq.abd.df$OTU, size=alpha2, replace=FALSE, prob=freq.abd.df$freq)), stringsAsFactors = F)
colnames(simcom2) = c("OTU","simcom2")
simcom2$OTU = as.character(simcom2$OTU)
simcom2 = inner_join(simcom2, freq.abd.df, by="OTU")
## Now recruit OTUs based on their abundance in the metacommunity
simcom1.abd = data.frame(table(sample(simcom1$OTU, size=N-alpha1, replace=TRUE, prob=simcom1$p)), stringsAsFactors = F)
colnames(simcom1.abd) = c("OTU","simcom1.abd")
simcom1.abd$OTU = as.character(simcom1.abd$OTU)
simcom1 = full_join(simcom1, simcom1.abd, by="OTU") %>%
mutate(simcom1.abd = ifelse(is.na(simcom1.abd), 1, simcom1.abd)) %>%
select(OTU, simcom1.abd)
simcom2.abd = data.frame(table(sample(simcom2$OTU, size=N-alpha2, replace=TRUE, prob=simcom2$p)), stringsAsFactors = F)
colnames(simcom2.abd) = c("OTU","simcom2.abd")
simcom2.abd$OTU = as.character(simcom2.abd$OTU)
simcom2 = full_join(simcom2, simcom2.abd, by="OTU") %>%
mutate(simcom2.abd = ifelse(is.na(simcom2.abd), 1, simcom2.abd)) %>%
select(OTU, simcom2.abd)
simcom = full_join(simcom1, simcom2, by="OTU")
simcom[is.na(simcom)] = 0
rownames(simcom) = simcom$OTU
simcom$OTU = NULL
null.dist = vegdist(t(simcom), method="bray")[1]
return(null.dist)
}
# 计算RCbray的主功能
Calc_RCbray <- function(physeq, reps, nproc){
# Get OTU table from phyloseq object
otu.table = otu_table(physeq)
# Get alpha diversity for each sample
otu.PA.table = otu.table
otu.PA.table[otu.PA.table > 0] = 1
alpha.df = data.frame(Sample_ID = colnames(otu.PA.table), OTU.n = colSums(otu.PA.table), stringsAsFactors = F)
# Get beta diversity matrix
beta.table = as.matrix(vegdist(t(otu.PA.table), method="bray", diag=TRUE, upper=TRUE))
## Get metacommunity
# Calculate the number of individuals in the meta community (Average read depth)
N <- mean(apply(t(otu.table), 1, sum))
# Calculate the average relative abundance of each taxa across communities
p.m <- apply(t(otu.table), 2, mean)
p.m <- p.m[p.m != 0]
p <- p.m/N
# Calculate the occurrence frequency of each taxa across communities
otu.table.bi <- 1*(t(otu.table)>0)
freq <- apply(otu.table.bi, 2, mean)
freq <- freq[freq != 0]
# Combine
freq.abd.df = data.frame(p=p, freq=freq) %>%
tibble::rownames_to_column(var="OTU") %>%
filter(p != 0, freq != 0) %>%
arrange(p)
# For each pair of samples run the RCbray analysis
comps = combn(alpha.df$Sample_ID, m=2, simplify = F)
RCb.df = data.frame(Site1 = character(), Site2 = character(), RCb = numeric(), stringsAsFactors = F)
for (j in seq(1, length(comps))){
sam = comps[[j]]
alpha1 = alpha.df[alpha.df$Sample_ID == sam[1],]$OTU.n
alpha2 = alpha.df[alpha.df$Sample_ID == sam[2],]$OTU.n
# Permute "reps" many times
rep.list = seq(1, reps)
null.list = mclapply(rep.list, RCbray_null_func, freq.abd.df=freq.abd.df, alpha1=alpha1, alpha2=alpha2, N=N, mc.cores=nproc)
RCb = (length(null.list[null.list > beta.table[sam[1], sam[2]]]) + (0.5*length(null.list[null.list == beta.table[sam[1], sam[2]]])))/reps
RCb = (RCb - 0.5)*2
RCb.df = rbind(RCb.df, data.frame(Site1=sam[1], Site2=sam[2], RCb=RCb, stringsAsFactors = F))
}
RCb.df
return(RCb.df)
}
# 运行RCbray的计算，这个运算再5个小时左右999重复
RCb = Calc_RCbray(psrare, num, thread)
head(RCb)
return(list(RCb))
}
result = RCbary(ps = psphy ,group = "Group",num = 10,thread = 1)
RCbary = result[[1]]
head(RCbary)
filename = paste(phypath,"/5_RCb.csv",sep = "")
write.csv(RCbary,filename)βNTI and RCbray combination analysis（βNTI和RCbray联合出图）
bNTIRCPlot = function(otu = NULL,tax = NULL,
map = NULL,tree = NULL ,
ps = NULL,
RCb = RCb,bNTI = bNTI,group = "Group"){
ps = inputMicro(otu,tax,map,tree,ps,group = group)
ps
psrare <- ps
map = as.data.frame(sample_data(psrare))
map$ID = row.names(map)
sample_data(psrare) = map
# Get habitat metadata and add it to the βNTI then merge with the RCbray dataset
eco.meta1 = data.frame(sample_data(psrare)) %>%
select(ID, Group) %>%
dplyr::rename(Sample_1 = ID, Group_1 = Group)
eco.meta2=data.frame(sample_data(psrare)) %>%
select(ID, Group) %>%
dplyr::rename(Sample_2 = ID, Group_2 = Group)
# bNTI 匹配第一列和第二列的分组信息
bNTI.df = inner_join(bNTI, eco.meta1) %>%
inner_join(eco.meta2)
# 合并两个数据
turnover.df = inner_join(bNTI.df, RCb)
head(turnover.df)
dim(turnover.df)
#--------------合并文件保存
# write.csv(turnover.df,"./Result/bNTI//bNTI_RCbray.csv")
#-----按照分组统计作图
#------------bNIT作图
dim(bNTI.df)
within.bNTI.df = bNTI.df %>%
filter(Group_1 == Group_2) %>%
mutate(Group = Group_1)
head(within.bNTI.df )
eco.bNTI.plot <- ggplot(within.bNTI.df, aes(x=Group, y=bNTI)) +
geom_jitter(alpha = 0.1,color ="#984EA3") +
geom_boxplot(outlier.shape=1,outlier.alpha = 0,fill = "#984EA3") +
geom_hline(yintercept = 2, linetype=2, size=0.5) +
geom_hline(yintercept = -2, linetype=2, size=0.5) +
labs(x="", y="bNTI") +
theme_classic() +
theme(legend.position = "none",
axis.text = element_text(size=12),
axis.text.x = element_text(angle=45, hjust=1),
axis.title = element_text(size=14))
# 现在按照RCbray进行分开标记系统发育过程
eco.turnover.df = turnover.df %>%
filter(Group_1 == Group_2) %>%
mutate(Group = Group_1)
head(eco.turnover.df )
## Calculate the relative influence of each process
eco.turnover.df = eco.turnover.df %>%
mutate(process = ifelse(abs(bNTI) < 2,
ifelse(abs(RCb) < 0.95, "Drift",
ifelse(RCb >= 0.95, "Dispersal Limited",
ifelse(RCb <= -0.95, "Homogenizing Dispersal", "ERROR"))),
ifelse(bNTI >= 2, "Variable Selection",
ifelse(bNTI <= -2, "Homogeneous Selection", "ERROR"))))
eco.turnover.df$process = factor(eco.turnover.df$process, levels = c("Drift",
"Dispersal Limited", "Homogenizing Dispersal",
"Variable Selection", "Homogeneous Selection"))
head(eco.turnover.df)
#------计算每个组的系统发育过程中五个部分分别占有的比例
pre = eco.turnover.df %>%
dplyr::group_by(Group, process) %>%
dplyr::summarize(n_sites = n(),
perc=(n()/45)*100) %>%
as.data.frame
# head(numeco )
numeco <- pre %>% dplyr::group_by(Group) %>%
dplyr::summarise(num = sum(n_sites))
alleco <- pre %>% dplyr::left_join(numeco,by = "Group")
alleco$perc = alleco$n_sites/ alleco$num * 100
sum.eco.turnover.df = alleco
eco.turnover.plot = ggplot(sum.eco.turnover.df, aes(x=Group, y=perc, fill=process)) +
geom_bar(stat="identity", color="black") +
# scale_fill_manual(values = c("white", "grey75", "grey50", "black")) +
labs(x="", y="Percent of pairs (%)", fill="Process") +
theme_bw() +
theme(panel.grid = element_blank(),
axis.text = element_text(size=12),
axis.text.x = element_text(angle=45, hjust=1),
axis.title = element_text(size=14),
legend.key.size = unit(10, "mm"),
legend.text = element_text(size=12),
legend.title = element_text(size=14))
eco.turnover.plot
# Merge the plots
eco.plot = cowplot::plot_grid(eco.bNTI.plot, eco.turnover.plot,
rel_widths=c(0.6, 1), labels=c("A", "B"))
eco.plot
return(list( eco.bNTI.plot, eco.turnover.plot,eco.plot,turnover.df,sum.eco.turnover.df))
}
bNTI = read.csv(paste(phypath,"/4_bNTI.csv",sep = ""),row.names = 1)
head(bNTI)# RCbray 数据读入，修改列名
RCb = read.csv(paste(phypath,"/5_RCb.csv",sep = ""),row.names = 1) %>%
dplyr::mutate(Sample_1 = Site2, Sample_2 = Site1)
head(RCb)
result = bNTIRCPlot(ps = psphy ,RCb = RCb,bNTI = bNTI,group = "Group")
#--bNTI出图片
p3 <- result[[1]]
p3
#RCbary可视化
p4 <- result[[2]]
p4
#组合图片BNTI，RCbray
p5 <- result[[3]]
p5
plotdata = result[[4]]
head(plotdata)
dat = result[[5]]
head(dat)
filename = paste(phypath,"/6_bNTI_RCbray.csv",sep = "")
write.csv(plotdata,filename)
FileName <- paste(phypath,"6_bNTI", ".pdf", sep = "")
ggsave(FileName, p3,width =8,height = 6)
FileName <- paste(phypath,"6_RCbary", ".pdf", sep = "")
ggsave(FileName, p4,width = 6,height = 6)
FileName <- paste(phypath,"6_BNTI_RCbray", ".pdf", sep = "")
ggsave(FileName, p5,width = 12,height = 8)
FileName <- paste(phypath,"6_bNTI", ".png", sep = "")
ggsave(FileName, p3,width =8,height = 6)
FileName <- paste(phypath,"6_RCbary", ".png", sep = "")
ggsave(FileName, p4,width = 6,height = 6)
FileName <- paste(phypath,"6_BNTI_RCbray", ".png", sep = "")
ggsave(FileName, p5,width = 12,height = 8)
FileName <- paste(phypath,"6_RCbray.percent.csv", sep = "")
write.csv(dat,FileName, quote = F)Environmental factors and βNTI correlation analysis（环境因子和βNTI相关分析）
EnvCorbNTI = function(otu = NULL,
tax = NULL,
map = NULL,
tree = NULL,
ps = NULL,
bNTIRC = RCbNTI,
env = env,
group = "Group"){
ps = inputMicro(otu,tax,map,tree,ps,group = group)
ps
#------------定义相关性分析函数
# df = data
Sams.mantel.test = function(df, seed=NULL) {
# Run mantel test to see if there is a correlation
delta.mat = df %>%
select(Sample_1, Sample_2, delta) %>%
spread(Sample_2, delta)
rownames(delta.mat) = delta.mat$Sample_1
delta.mat$Sample_1 = NULL
delta.mat = delta.mat[names(sort(rowSums(!is.na(delta.mat)), decreasing = FALSE)), names(sort(colSums(!is.na(delta.mat)), decreasing = TRUE))]
delta.mat = as.dist(delta.mat)
bNTI.mat = df %>%
select(Sample_1, Sample_2, bNTI) %>%
spread(Sample_2, bNTI)
rownames(bNTI.mat) = bNTI.mat$Sample_1
bNTI.mat$Sample_1 = NULL
bNTI.mat = bNTI.mat[names(sort(rowSums(!is.na(bNTI.mat)), decreasing = FALSE)), names(sort(colSums(!is.na(bNTI.mat)), decreasing = TRUE))]
bNTI.mat = as.dist(bNTI.mat)
if (!(is.null(seed))){
set.seed(seed)
}
bNTI.mat[is.na(bNTI.mat)] = 0
mantel.res = vegan::mantel(delta.mat, bNTI.mat)
return(mantel.res)
}
set.seed(72) # setting seed for reproducibility
psrare = rarefy_even_depth(ps)
#检查序列数量
sample_sums(psrare)
# 标准化数据
ps.norm = transform_sample_counts(psrare, function(x) x/sum(x))
map = as.data.frame(sample_data(psrare))
# map = data.frame(row.names = map$id,id = map$id,Group = map$Group)
mapE =merge(map,env,by = "row.names",all= FALSE)
row.names(mapE) = mapE$Row.names
mapE$Row.names = NULL
mapE$ID = row.names(mapE)
head(mapE)
#---------合并环境变量数据
# i = "Altitude..m."
plot = list()
for (i in colnames(env)) {
colnames(mapE) = gsub(i,"XX",colnames(mapE))
# Add in pH metadata
pH.meta1=mapE %>%
dplyr::select(ID, XX) %>%
dplyr::rename(Sample_1 = ID, env1_1 = XX)
pH.meta2= mapE%>%
dplyr::select(ID, XX) %>%
dplyr::rename(Sample_2 = ID, env1_2 = XX)
data = dplyr::inner_join(bNTIRC, pH.meta1) %>%
dplyr::inner_join(pH.meta2) %>%
dplyr::mutate(delta = abs(env1_1-env1_2),
crosstype = ifelse(Group_1 == Group_2, as.character(Group_1), "across"))
head(data)
data$crosstype
# Run mantel test to see if there is a correlation
pH.mantel = Sams.mantel.test(data, seed=72)
head(data)
# Plot
p = ggplot(data, aes(x=delta, y=bNTI)) +
geom_point(pch = 21) +
# scale_shape_manual(values=LandUse.shapes) +
geom_hline(yintercept = 2, linetype=2) +
geom_hline(yintercept = -2, linetype=2) +
# annotate("text", x=3.25, y=12.5, label=paste("r= ", round(pH.mantel$statistic, 3), "\n", "p= ", round(pH.mantel$signif, 3), sep="")) +
labs(x=paste("",i), y="βNTI",title = paste("r= ", round(pH.mantel$statistic, 3), "p= ", round(pH.mantel$signif, 3))) +
theme(legend.position = "none") +theme_bw()
p
plot[[i]] = p
colnames(mapE) = gsub("XX",i,colnames(mapE))
}
library(ggpubr)
p = ggarrange(plotlist = plot, common.legend = TRUE, legend="right")
p
return(list(p,plot))
}
Sams.mantel.test = function(df, seed=NULL) {
# Run mantel test to see if there is a correlation
delta.mat = df %>%
select(Sample_1, Sample_2, delta) %>%
spread(Sample_2, delta)
rownames(delta.mat) = delta.mat$Sample_1
delta.mat$Sample_1 = NULL
delta.mat = delta.mat[names(sort(rowSums(!is.na(delta.mat)), decreasing = F)), names(sort(colSums(!is.na(delta.mat)), decreasing = T))]
delta.mat = as.dist(delta.mat)
bNTI.mat = df %>%
select(Sample_1, Sample_2, bNTI) %>%
spread(Sample_2, bNTI)
rownames(bNTI.mat) = bNTI.mat$Sample_1
bNTI.mat$Sample_1 = NULL
bNTI.mat = bNTI.mat[names(sort(rowSums(!is.na(bNTI.mat)), decreasing = F)), names(sort(colSums(!is.na(bNTI.mat)), decreasing = T))]
bNTI.mat = as.dist(bNTI.mat)
if (!(is.null(seed))){
set.seed(seed)
}
bNTI.mat[is.na(bNTI.mat)] = 0
mantel.res = vegan::mantel(delta.mat, bNTI.mat)
return(mantel.res)
}
# df = data
Sams.mantel.test = function(df, seed=NULL) {
# Run mantel test to see if there is a correlation
delta.mat = df %>%
select(sample1, sample2, delta) %>%
spread(sample2, delta)
rownames(delta.mat) = delta.mat$sample1
delta.mat$sample1 = NULL
delta.mat = delta.mat[names(sort(rowSums(!is.na(delta.mat)), decreasing = F)), names(sort(colSums(!is.na(delta.mat)), decreasing = T))]
delta.mat = as.dist(delta.mat)
bNTI.mat = df %>%
select(sample1, sample2, bNTI) %>%
spread(sample2, bNTI)
rownames(bNTI.mat) = bNTI.mat$sample1
bNTI.mat$sample1 = NULL
bNTI.mat = bNTI.mat[names(sort(rowSums(!is.na(bNTI.mat)), decreasing = F)), names(sort(colSums(!is.na(bNTI.mat)), decreasing = T))]
bNTI.mat = as.dist(bNTI.mat)
if (!(is.null(seed))){
set.seed(seed)
}
bNTI.mat[is.na(bNTI.mat)] = 0
mantel.res = vegan::mantel(delta.mat, bNTI.mat)
return(mantel.res)
}
#-导入bNTI函数
bNTIRC = read.csv(paste(phypath,"/6_bNTI_RCbray.csv",sep = ""),row.names = 1)
head(bNTIRC)
map = sample_data(psphy)
# head(map)
plot = EnvCorbNTI(ps = psphy,
bNTIRC = bNTIRC,
group = "Group",
env = envRDA
)
## 提取相关分析结果，总图
p6 <- plot[[1]]
p6
#提取单个
# plot[[2]][1]
FileName <- paste(phypath,"7_env_corWithBNTI", ".pdf", sep = "")
ggsave(FileName, p6,width = 16,height = 14)
FileName <- paste(phypath,"7_env_corWithBNTI", ".png", sep = "")
ggsave(FileName, p6,width = 16,height = 14)